Networks of Control | Cracked Labs

!


Wolfie Christl and Sarah Spiekermann; Networks of Control; Facultas, Vienna; 2016; 185 pages; landing.
Teaser: A Report on Corporate Surveillance, Digital Tracking, Big Data & Privacy

Table of Contents

  1. Preface
  2. Introduction
  3. Analyzing Personal Data
    1. Big Data and predicting behavior with statistics and data mining
    2. Predictive analytics based on personal data: selected examples
      1. The “Target” example: predicting pregnancy from purchase behavior
      2. Predicting sensitive personal attributes from Facebook Likes
      3. Judging personality from phone logs and Facebook data
      4. Analyzing anonymous website visitors and their web searches
      5. Recognizing emotions from keyboard typing patterns
      6. Forecasting future movements based on phone data
      7. Predicting romantic relations and job success from Facebook data
    3. De-anonymization and re-identification
  4. Analyzing Personal Data in Marketing, Finance, Insurance and Work
    1. Practical examples of predicting personality from digital records
    2. Credit scoring and personal finance
    3. Employee monitoring, hiring and workforce analytics
    4. Insurance and healthcare
    5. Fraud prevention and risk management
    6. Personalized price discrimination in e-commerce
  5. Recording Personal Data – Devices and Platforms
    1. Smartphones, mobile devices and apps – spies in your pocket?
    2. Car telematics, tracking-based insurance and the Connected Car
      1. Data abuse by apps
    3. Wearables, fitness trackers and health apps – measuring the self
      1. A step aside – gamification, surveillance and influence on behavior
      2. Example: Fitbit’s devices and apps
      3. Transmitting data to third parties
      4. Health data for insurances and corporate wellness
    4. Ubiquitous surveillance in an Internet of Things?
      1. Examples – from body and home to work and public space
  6. Data Brokers and the Business of Personal Data
    1. The marketing data economy and the value of personal data
    2. Thoughts on a ‘Customers’ Lifetime Risk’ – an excursus
    3. From marketing data to credit scoring and fraud detection
    4. Observing, inferring, modeling and scoring people
    5. Data brokers and online data management platforms
    6. Cross-device tracking and linking user profiles with hidden identifiers
    7. Case studies and example companies
      1. Acxiom – the world’s largest commercial database on consumers
      2. Oracle and their consumer data brokers Bluekai and Datalogix
      3. Experian – expanding from credit scoring to consumer data
      4. arvato Bertelsmann – credit scoring and consumer data in Germany
      5. LexisNexis and ID Analytics – scoring, identity, fraud and credit risks
      6. Palantir – data analytics for national security, banks and insurers
      7. Alliant Data and Analytics IQ – payment data and consumer scores
      8. Lotame – an online data management platform (DMP)
      9. Drawbridge – tracking and recognizing people across devices
      10. Flurry, InMobi and Sense Networks – mobile and location data
      11. Adyen, PAY.ON and others – payment and fraud detection
      12. MasterCard – fraud scoring and marketing data
  7. Summary of Findings and Discussion of its Societal Implications
    1. Ubiquitous data collection
    2. A loss of contextual integrity
    3. The transparency issue
    4. Power imbalances
    5. Power imbalances abused: systematic discrimination and sorting
    6. Companies hurt consumers and themselves
    7. Long term effects: the end of dignity?
    8. Final reflection: From voluntary to mandatory surveillance?
  8. Ethical Reflections on Personal Data Markets (by Sarah Spiekermann)
    1. A short Utilitarian reflection on personal data markets
    2. A short deontological reflection on personal data markets
    3. A short virtue ethical reflection on personal data markets
    4. Conclusion on ethical reflections
  9. Recommended Action
    1. Short- and medium term aspects of regulation
    2. Enforcing transparency from outside the “black boxes”
    3. Knowledge, awareness and education on a broad scale
    4. A technical and legal model for a privacy-friendly digital economy
  10. List of tables
  11. List of figures
  12. References

Mentions

yes

Quoted

  • Anna Fielder, Chair of Privacy International
  • Courtney gabrielson, International Association of Privacy Professionals (IAPP)

References

There are 677 footnoes, which are distinct from the references.
There are 211 references.

Separately filled.

Corporate Surveillance in Everyday Life | Cracked Labs


Corporate Surveillance in Everyday Life. How Companies Collect, Combine, Analyze, Trade, and Use Personal Data on BillionsWolfie Christl,; Cracked Labs, Vienna; 2017-06; 93 pages.

Teaser: <shrill>How thousands of companies monitor, analyze, and influence the lives of billions. Who are the main players in today’s digital tracking? What can they infer from our purchases, phone calls, web searches, and Facebook likes? How do online platforms, tech companies, and data brokers collect, trade, and make use of personal data?</shrill>

Table of Contents

  1. Background and Scope
  2. Introduction
  3. Relevant players within the business of personal data
    1. Businesses in all industries
    2. Media organizations and digital publishers
    3. Telecom companies and Internet Service Providers
    4. Devices and Internet of Things
    5. Financial services and insurance
    6. Public sector and key societal domains
    7. Future developments?
  4. The Risk Data Industry
    1. Rating people in finance, insurance and employment
    2. Credit scoring based on digital behavioral data
    3. Identity verification and fraud prevention
    4. Online identity and fraud scoring in real-time
    5. Investigating consumers based on digital records
  5. The Marketing Data Industry
    1. Sorting and ranking consumers for marketing
    2. The rise of programmatic advertising technology
    3. Connecting offline and online data
    4. Recording and managing behaviors in real-time
    5. Collecting identities and identity resolution
    6. Managing consumers with CRM, CIAM and MDM
  6. Examples of Consumer Data Broker Ecosystems
    1. Acxiom, its services, data providers, and partners
    2. Oracle as a consumer data platform
    3. Examples of data collected by Acxiom and Oracle
  7. Key Developments in Recent Years
    1. Networks of digital tracking and profiling
    2. Large-scale aggregation and linking of identifiers
    3. “Anonymous” recognition
    4. Analyzing, categorizing, rating and ranking people
    5. Real-time monitoring of behavioral data streams
    6. Mass personalization
    7. Testing and experimenting on people
    8. Mission creep – everyday life, risk assessment and marketing
  8. Conclusion
  9. Figures
  10. References

Mentions

Quoted

  • Omer Tene
  • Jules Polonetsky

Promotions

Yes.  A work this polished could be hid for long.

Summary

The web variant is summary material.

  1. Analyzing people
  2. Analyzing people in finance, insurance and healthcare
  3. Large-scale collection and use of consumer data
  4. Data brokers and the business of personal data
  5. Real-time monitoring of behaviors across everyday life
  6. Linking, matching and combining digital profiles
  7. Managing consumers and behaviors, personalization and testing
  8. Dragnet – everyday life, marketing data and risk analytics
  9. Mapping the commercial tracking and profiling landscape
  10. Towards a society of pervasive digital social control?

References

There are 601 footnotes, which are distinct from the references.
There are 102 of references

Previously filled.

Living on Fumes: Digital Footprints, Data Fumes, and the Limitations of Spatial Big Data | Jim Thatcher

Jim Thatcher (Clark University); Living on Fumes: Digital Footprints, Data Fumes, and the Limitations of Spatial Big Data; In International Journal of Communications (IJC); Volume 8; 2014; 19 pages; landing; previously in Proceedings of the 26th International
Cartographic Conference (ICC), 2014.

tl;dr → whereas capitalism is bad, the critical theory: sociotechnical, epistemic project, abductive processes, epistemic limits, epistemic and ontological commitments, capitalist profit motives, private corporations; frameworks of Marcuse, Pickles. You get the idea.

Abstract

Amid the continued rise of big data in both the public and private sectors, spatial information has come to play an increasingly prominent role. This article defines big data as both a sociotechnical and epistemic project with regard to spatial information. Through interviews, job shadowing, and a review of current literature, both academic researchers and private companies are shown to approach spatial big data sets in analogous ways. Digital footprints and data fumes, respectively, describe a process that inscribes certain meaning into quantified spatial information. Social and economic limitations of this data are presented. Finally, the field of geographic information science is presented as a useful guide in dealing with the “hard work of theory” necessary in the big data movement.

Mentions

  • In the introductory paragraph, cites opinements in Fast Company and Mashable as authoritative directional indicators.
  • Two problems
    1. <quote>On the one hand, rather than fully capturing life as researchers hope, end-user interactions within big data are necessarily the result of decisions made by an extremely small group of programmers working for private corporations that have [been] promulgated through the mobile application ecosystem.
    2. On the other hand, in accepting that the data gathered through mobile applications reveal meaningful information about the world, researchers are tacitly accepting a commodification and quantification of knowledge.</quote>
  • Big Data is
    • (wait for it …) very big, “large” even.
    • <quote>data whose size forces us to look beyond the tried-and-true methods
      that are prevalent at that time</quote>, Adam Jacobs.
    • Contrarianism
      • Something vague about Taylorism, Max Weber, etc.
      • Something vague about how having more data is better, or is not better.
    • The Fourth Paradigm
      1. empiricism
      2. analysis
      3. simulation.
      4. explore & exploit
    • Sources
      <quote>Most current studies describing themselves as “big data” with a spatial component revolve around two mobile software platforms [Foursquare, Twitter]</quote>

      • Foursquare
      • Twitter
      • Facebook
      • Flickr
  • Types of Data [plural of types of Datum(s)]
    • Checkin
    • Tweet
  • Livehood
  • 25% of Foursquare users link their Twitter accounts (75% don’t)
  • <quote>Finally, the reliance upon data generated with an explicit motive for profit — both for the end user and the corporation—results in epistemological commitments not dissimilar to concerns raised with regard to the knowledges and approaches privileged by GIS use. </quote>
  • <quote>This hard work of theory opens new knowledge projects within the realm of big data. For example, if the check-in is viewed as a form of disciplining technology — one that reports location to enmesh it more fully in capitalist exchange — then purposeful location fraud takes on new meaning as a potential form of resistance or protest.</quote>

Badness

  • private companies
  • profit motives
  • capitalism

Metaphors

  • Digital footprints
  • Digital fumes

Technologies

  • PostgreSQL
  • R
  • Mac (OS)

References

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  • Berry, D. M. (2011). The philosophy of software: Code and mediation in the digital age. London, UK: Palgrave Macmillan.
  • boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679.
  • Brownlee, J. (2012-03-30). This creepy app isn’t just stalking women without their knowledge, it’s a wake-up call about Facebook privacy. In Cult of Mac.
  • Burgess, J., & Bruns, A. (2012). Twitter archives and the challenges of “big social data” for media and communication research. M/C Journal, 15(5).
  • Carbunar, B., & Potharaju, R. (2012). You unlocked the Mt. Everest badge on Foursquare! Countering location fraud in geosocial networks. In Proceedings of the 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), pages 182-190. IEEE Computer Society, Washington, DC.
  • Cerrato, P. (2012-11-01). Big data analytics: Where’s the ROI? InformationWeek: Healthcare.
  • Cheng, Z., Caverlee, J., Lee, K., & Sui, D. (2011). Exploring millions of footprints in location sharing services. In Proceedings of the Fifth International AAAI Conference on WSM. Barcelona, Spain.
  • Crampton, J. (2003). The political mapping of cyberspace. Edinburgh, Scotland: Edinburgh University Press.
  • Crampton, J. (2013). Commentary: Is security sustainable? Environment and Planning D, 31, 571–577.
  • Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2012). The Livehoods Project: Utilizing social media to understand the dynamics of a city. In Proceedings of the Sixth International AAAI Conference on WSM. Dublin, Ireland.
  • Curry, M. (1997). The digital individual and the private realm. Annals of the AAG, 87, 681–699.
  • DeLyser, D., & Sui, D. (2013). Crossing the qualitative-quantitative divide II: Inventive approaches to big data, mobile methods, and rhythmanalysis. Progress in Human Geography, 37(2), 293–305.
  • Eckert, J., & Hemsley, J. (2013-04-11). Occupied Reographies, Relational or Otherwise. Presentation to the American Association of Geographers, Los Angeles, CA.
  • Exner, J., Zeile, P., & Streich, B. (2011). Urban monitoring laboratory: New benefits and potential for urban planning through the use of urban sensing, geo- and mobile-web. In Proceedings of Real CORP 2011. pages 1087–1096. Wien, Austria.
  • Farmer, C., & Pozdnoukhov, A. (2012). Building streaming GIScience from context, theory, and intelligence. In Proceedings of the Workshop on GIScience in the Big Data Age 2012. pages 5–10. Columbus, Ohio.
  • Goodchild, M. (1992). Geographical information science. In International Journal of Geographical Information Systems, 6, 31–45.
  • Goodchild, M. (2007). Citizens as sensors: The world of volunteered geography. In GeoJournal, 69(4), 211– 221.
  • Goodchild, M., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: A research frontier. In International Journal of Digital Earth, 3(3), 231–241.
  • Harley, J. (1989). Deconstructing the map. In Cartographical, 26, 1–20.
  • Hecht, B., Hong, L., Suh, B., & Chi, E. (2011). Tweets from Justin Bieber’s heart. In Proceedings of the ACM CHI Conference 2011. pages 237–246. Vancouver, BC.
  • Heidegger, M. (1977). The question concerning technology and other essays. W. Lovitt, Translator. New York, NY: Harper Perennial.
  • Hey, T., Tansley, S., & Toelle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Richmond, WA: Microsoft Research.
  • Horvath, I. (2012). Beyond advanced mechatronics: New design challenges of social-cyber systems. (Draft paper.) In Proceedings of the ACM Workshop on Mechatronic Design, Linz 2012. Linz, Austria
  • Jacobs, A. (2009). The pathologies of big data. In ACM Queue, 7(6), pages 1–12.
  • Joseph, K., Tan, C., & Carley, K. (2012). Beyond “local,” “categories” and “friends”: Clustering Foursquare users with latent “topics.” In Proceedings of ACM Ubicomp 2012. pages 919–926. Pittsburgh, PA.
  • Kingsbury, P., & Jones III, J. P. (2009). Walter Benjamin’s Dionysian adventures on Google earth. In Geoforum, 40, 502–513.
  • Kitchin, R., & Dodge, M. (2007). Rethinking maps. In Progress in Human Geography, 31, 331–344.
  • Kling, F., & Pozdnoukhov, A. (2012). When a city tells a story: Urban topic analysis. In Proceedings of ACM SIGSPATIAL 2012. pages 482–485. Redondo Beach, CA.
  • Lathia, N., Quercia, D., & Crowcroft, J. (2012). The hidden image of the city: Sensing community well-being from urban mobility. In Pervasive Computing,/em>, 7319, 91–98.
  • Laurila, J., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T., Dousse, O., Eberle, J., & Miettinen, M. (2012). The mobile big data challenge. Nokia Research.
  • Livehoods, demonstrator & promotional site. (2012).
  • Lohr, S. (2012-12-29). Sure, big data is great. But so is intuition. The New York Times.
  • Long, X., Jin, L., & Joshi, J. (2012). Exploring trajectory-driven local geographic topics in Foursquare. In Proceedings of ACM Ubicomp 2012. pages 927–934. Pittsburgh, PA.
  • Marcuse, H. (1982 [1941]). Some social implications of modern technology. In A. Arato & E. Gebhardt (Eds.), The essential Frankfurt School reader. pages 138–162. New York, NY: Continuum.
  • Martino, M., Britter, R., Outram, C., Zacharias, C., Biderman, A., & Ratti, C. (2010). Senseable city. Cambridge, MA: MIT Senseable City Lab.
  • Mayer-Schonberger, V., & Cukier, K. (2013). Big Data: A revolution that will transform how we live, work, and think. New York, NY: Houghton Mifflin Harcourt.
  • Mitchell, J. (2012-04-10). Life after death of the check-in. In ReadWrite.
  • National Science Foundation. (2012-10-03). NSF announces interagency progress on administration’s= big data initiative. Press release.
  • Noulas, A., Scellato, S., Mascolo, C., & Pontil, M. (2011). An empirical study of geographic user activity patterns in Foursquare. In Proceedings of the Fifth International AAAI Conference on WSM. pages 570–573. Barcelona, Spain.
  • Obermeyer, N. (1995). The hidden GIS technocracy. In Cartography and Geographic Information Science, 22(1), 78–83.
  • O’Sullivan, D. (2006). Geographical information science: Critical GIS. Progress in Human Geography, 30(6), 783–791.
  • Paulos, E., Honicky, R. J., & Hooker, B. (2008). Citizen science: Enabling Participatory Urbanism. In M. Foth (Ed.), Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City. pages 414–436. Hershey, PA: Information Science Reference.
  • Pickles, J. (1993). Discourse on method and the History of Discipline: Reflections on Jerome Dobson’s 1993 “Automated geography.” In Professional Geographer, 45, 451–455.
  • Pickles, J. (1995). Ground Truth. New York, NY: Guilford Press.
  • Pickles, J. (1997). Tool or Science? GIS, Technoscience and the Theoretical Turn. In Annals of the AAG, 87, pages 363–372.
  • Presley, S. (2011). Mapping out #LondonRiots. In NFPvoice.
  • Rasmus, D. (2012-01-27). Why big data won’t make you smart, rich, or pretty. In Fast Company.
  • Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW). pages 851–860. Raleigh, NC.
  • A sea of sensors. staff; (2010-11-04). In The Economist.
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  • Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Hoboken, NJ: John Wiley.
  • Sui, D. (2008). The wikification of GIS and its consequences: Or Angelina Jolie’s new tattoo and the future of GIS. In Computers, Environment and Urban Systems, 32, 1–5.
  • Taylor, C. (2012-11-07). Triumph of the nerds: Nate Silver wins in 50 states. In Mashable.
  • Thatcher, J. (2013-12). Avoiding the Ghetto through hope and fear: An analysis of immanent technology using ideal types. In GeoJournal. Volume 78, Issue 6, pages 967-980. paywall.
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  • Xu, S., Flexner, S., & Carvalho, V. (2012). Geocoding billions of addresses: Towards a spatial record linkage system with big data. In Proceedings of the Workshop on GIScience in the Big Data Age 2012. pages 17–26. Columbus, Ohio.

Actualities

Via: backfill.

Web Privacy Census | Altaweel, Good, Hoofnagle

Ibrahim Altaweel, Nathaniel Good, Chris Jay Hoofnagle; Web Privacy Census; In Technology Science; 2015-12-15.

tl;dr → there are lots of (HTML4) cookies; cookies are for tracking; cookies are bad. factoids are exhibited.

Abstract

Most people may believe that online activities are tracked more pervasively now than they were in the past. In 2011, we started surveying the online mechanisms used to track people online (e.g., HTTP cookies, Flash cookies and HTML5 storage). We called this our Web Privacy Census. We repeated the study in 2012. In this paper, we update the study to 2015.

Mentions

  • Universe
    • Quantcast
    • “top 1 million”
  • Attack
    • Firefox 39
    • OpenWPM
  • Client
    • HTML4 Cookies
    • HTML5 Storage
    • Flash
  • Use Cases
    indistinguishable in the census method

    • Analytics
    • Tracking (Trak-N-Targ)
    • Conversion
    • Personalization
    • Security

References

Continued Compendium on Ad Blocking in Advertising Age through 2015-09-15

Continued from the Compendium through 2015-12-xxx

In Advertising Age circa 2015-09-14

Previously

Compendium on Ad Blocking in Advertising Age through 2015-09-05

Currently


Why Ad-Blocking Is Good News for Almost Everyone; (Havas); 2015-09-15.
Teaser: Apple’s Move to Block Mobile Ads Will Force Advertisers to Rethink Mobile

Tom Goodwin,
senior VP-strategy and innovation, Havas Media, New York.
ex-founder, director, Tomorrow Group, London.

Mentioned

  • a contrarian view
  • Apple
  • iO 9
  • <quote>The surprisingly, rarely challenged, assumption in advertising has always been that there should be a relatively close correlation between time spent in a channel and the advertising spend within it. So as we spend more of our lives staring into our smartphones, the need for marketers to spend more money on mobile grows by the day.</quote>

Tactics

as a listicle

  1. Premium mobile advertising
    e.g. Superbowl ads, Vogue (magazine) ads
  2. insidious advertising
    native ads, advertorials branded content
  3. Branded utility
    apps; e.g. Michelin guide

So Which Ad-Blocking Parasite Are You Going to Go After?; ; 2015-09-14.
Teaser: Convince Consumers or Sue the Ad-Blocking Companies; You Have to Do Something
Ken Wheaton, editor, Advertising Age

tl;dr → equates ad blocking with theft.

Mentioned

  • very shrill [very very shrill], very angry
  • <quote>But it’s a bad idea to believe that consumers care much about the plight of marketers or publishers.</quote>
  • <quote>The worst possible response, however, is paying an ad-blocking company or an anti-ad-blocking company money to get ads past filters and in front of the viewer. </quote>
  • <quote>I can’t quite believe I’m saying this, but how about suing the ad blockers out of existence?</quote>
  • <quote>But as WPP Digital President and Xaxis Chairman David Moore, who also serves as chairman of the board of directors for the IAB Tech Lab, points out, the ad blockers “are interfering with websites’ ability to display all the pixels that are part of that website; arguably there’s some sort of law that prohibits that.</quote>
  • <quote>But theft is still theft, even if it’s dressed up as some sort of digital Robin Hood act. You’re not just interfering with pixels, you’re interfering with business.</quote>

Memes, Argot

  • the consumer is in control
  • ad skipping
  • hyper-targeted, data-fueled ad environment
  • banner blindness
  • extortion

Yes, There Is a War on Advertising. Now What?; , ; 2015-09-14.
Teaser: Ads Are Being Cast as the Enemy as Consumers Find More and More Ways to Block Them

Mentioned

  • Apple
  • iOS 9
  • Numerics towards the prevalence of ad blocking are recited.
    • Brian Wieser, staff, Pivotal Research Group.
    • ComScore’s U.S. Mobile App Report.
    • eMarketer
  • AdBlock Mobile
  • Eyeo
  • Adblock Plus
  • Howard Stern
    promoted Ad ad blocking, as a concept, on his show.
  • Responses
    • Hulu → block consumers who block ads
    • Washington Post → some trials, push consumers to subscribe, to whitelist the site & its ads
  • Countermeasures
    • PageFair
    • Secret Media
    • Sourcepoint
    • Yavli
  • TrueX
    • Acquired by Fox Networks Group, 2014-12.
    • Joe Marchese, founder
  • Fox Networks Group
    • branded content
    • show: “MasterChef Junior”
      sponsored by: California Milk Advisory Board.

Referenced

credulously, as authoritative

Yet

Quoted

for color, background & verisimilitude

  • Dan Jaffe, lobbyist, Association of National Advertisers (ANA)
  • Scott Cunningham, senior VP, Interactive Advertising Bureau (IAB); general manager, Tech Lab, Interactive Advertising Bureau (IAB).
  • David Moore, President, WPP Digital; Chairman, Xaxis; Chairman of the Board of Directors, Tech Lab, Interactive Advertising Bureau (IAB).
  • Joe Marchese, president-advanced ad products, Fox Networks Group.
  • Brian Wieser, staff, Pivotal Research Group.

Confusion Reigns as Apple Puts the Spotlight on Mobile Ad Blocking; Maureen Morrison; In Ad Age; 2015-09-08.
Teaser: Mobile Ad Blocking Is Present and Effective Before Apple Updates a Thing

tl;dr → reprise, same material

 

Via: backfill.

Sleeping Through a Revolution | Jonathan Taplin

  • Jonathan Taplin; The Technology Revolution Impacts and Reduces the Workforce; On YouTube; 2015-03-10; 5:06.
  • Jonathan Taplin; Sleeping Through a Revolution; on Vimeo; 2015-03-10; 44:10.
    Teaser: The Moral Framework of the Technology Revolution
  • Jonathan Taplin (USC); Sleeping Through a Revolution; In Medium; 2015-04-22.
    Teaser: Letter to the Millennials 2

tl;dr → internet advertising is bad; internet surveillance is bad; an extended defense of high-copyright cultural products industries (music, film, etc.).  Google is bad.

Proposal

Platform for the Renaissance
  • 1GB/s symmetric network
  • Network Neutrality
  • Regulation
  • Copyright on everything
  • Public broadcasting
  • Micropayments
On the micropayments concept
  • which is not advertising
  • with no embedded clearance fees
    cited as e.g. Visa, PayPal, Bitcoin, etc.
    ahem, because … the moneychangers don’t create.
  • with fees for cultural product presentment
    cited as, e.g. $0.25/view to read the video/audio/linkbait/UGC ($250 CPM).
    ahem, sounds very Randian

Mentions

(discursive, rambling)

  • Annenberg Innovation Lab, University of Southern California.
  • Recitation of the ’60s and ’70s counterculture as a time of greatness
    • Chroniclers
      • Fred Turner
      • John Markoff
      • Nicholas Negroponte
    • Whole Earth Lectronic Link (WELL)
    • commune
    • Ken Kesey
    • Stewart Brand
  • Recitation of the ’80s and beyond as a time of badness
    • Peter Thiel, PayPall
    • the Stanford University cohort
    • Silicon Valley
    • Ayn Rand
    • The PayPal Mafia
      • all men, as an epithet
    • The Cato Institute
    • male makers
    • Larry Page, ex-CEO, Google
    • Jeff Bezos, CEO, Amazon
    • Napster
    • internet platform
  • Scott Timberg; Culture Crash: The Killing of the Creative Class; Yale University Press; 2015-01-13; 320 pages; kindle: $13, paper: $12+SHT.
  • Ethan Zuckerman; Rewire: Digital Cosmopolitans in the Age of Connection; W. W. Norton & Company; 2013-06-17; 288 pages; kindle: $10, paper: $8+SHT.
  • Robert Scheer; They Know Everything About You: How Data-Collecting Corporations and Snooping Government Agencies Are Destroying Democracy; Nation Books; 2015-02-24; 272 pages; kindle: $15, paper: $10+SHT.
  • Monopolies
    • Government-defined monopolies → good (AT&T, etc.)
    • Unregulated (natural) monopolies → bad (Apple, Comcast, Facebook, Google, etc.)
  • Epithets
    • Digital Bandits
      • Kim Dotcom
    • Svengali
      • David Plouffe
  • George Akerlof → market for lemons
  • YouTube isn’t quality content, those people aren’t true artists.
    Hollywood film is quality content made by true artists.
  • Quoted
    for color, background & verisimilitude

    • Nils Gilman, Associate Chancellor, UC Berkeley
    • Larry Summers, Harvard
  • Nils Gilman (UCB); The Twin Insurgency; In The American Interest; Volume 9, Number 6; 2014-06-15.
    Teaser: The postmodern state is under siege from plutocrats and criminals who unknowingly compound each other’s insidiousness.
    <quote>The postmodern state is under siege from plutocrats and criminals who unknowingly compound each other’s insidiousness.</quote>
  • Cited, as exemplars of extreme good or evil
    • Abraham Lincoln
    • ISIS
  • sharing economy
  • Airbnb
  • TaskRabbit
  • Uber
  • David Plouffe, lobbyist, ex-Obama 2012
  • The Koch Brothers
  • Stop Online Piracy Act (SOPA)
    • was good
    • crude, but
  • Some article, The Economist (uncited)
    the ability to substitute capital for labor (has profound implications)
  • Reagan, Reagan-era
  • John Maynard Keynes
    opined about substituting capital for labor (the 15 hour work week)
  • Martin Luther King
    credited with the quote “asleep at the reolution”
  • Julie Cohen, professor, Georgetown University
    • opined about privacy
    • popularization, summarization
      Why does Privacy Matter?  One Scholar’s Answer; Jathan Sadowski; In The Atlantic; 2013-02-26.
      Teaser: If we want to protect privacy, we should be more clear about why it is import
      tl;dr → <quote>Privacy is not just something we enjoy. It is something that is necessary for us to: develop who we are; form an identity that is not dictated by the social conditions that directly or indirectly influence our thinking, decisions, and behaviors; and decide what type of society we want to live in.</quote>
  • Aldous Huxley
  • Virtual Reality’s Potential Displayed at Game Developers’ Conference; In The New York Times (NYT); 2015-03-06.
  • Nir Eyal; Hooked: How to Build Habit Forming Products; Nir Eyal, via Amazon; 2013-12-30; 156 pages; kindle: $14, paper: $12+SHT.
  • Sundance Courts a New Celebrity Crowd; some cub reporter; In The New York Times (NYT); 2015-02-01.
    tl;dr → Sundance Film Festival, grift, bribes for promotion
  • Liberty
    • Libertarian liberty → bad
      the absence of non-consensual oversight
    • Thomas Jefferson liberty → good
      of Life, Liberty & the Pursuit of Happiness
  • Thomas Jefferson
  • Government has a role to play
  • American renaissance: 1935→1975.
  • Plato
  • Vox Media
  • BuzzFeed
  • Argot
    • native advertising
    • brand integration
  • Facebook
  • Artists
    the good guys

    • Bob Dylan
    • George Harrison
    • Martin Scorsese

Via: backfill, backfill.

The Technology Revolution Impacts and Reduces the Workforce | Jon Taplin @ USC

The Technology Revolution Impacts and Reduces the Workforce | Jon Taplin @ USC; editor; In Some Blog, entitled The Trichordist; 2015-08-21.

Original Sources

  • Jonathan Taplin; The Technology Revolution Impacts and Reduces the Workforce; On YouTube; 2015-03-10; 5:06.
  • Jonathan Taplin; Sleeping Through a Revolution; on Vimeo; 2015-03-10; 44:10.
  • Jonathan Taplin (USC); Sleeping Through a Revolution; In Medium; 2015-04-22.
    Teaser: Letter to the Millennials 2

Mentions

(discursive, rambling)

  • Annenberg Innovation Lab, University of Southern California.
  • Recitation of the ’60s and ’70s counterculture as a time of greatness
    • Chroniclers
      • Fred Turner
      • John Markoff
      • Nicholas Negroponte
    • Whole Earth Lectronic Link (WELL)
    • commune
    • Ken Kesey
    • Stewart Brand
  • Recitation of the ’80s and beyond as a time of badness
    • Peter Thiel, PayPall
    • the Stanford University cohort
    • Silicon Valley
    • Ayn Rand
    • The PayPal Mafia
    • The Cato Institute
    • male maqkers
    • Larry Page, ex-CEO, Google
    • Jeff Bezos, CEO, Amazon
    • Napster
    • internet platform
  • Epithets
    • Digital Bandits
      • Kim Dotcom
    • Svengali
      • David Plouffe
  • Quoted
    for color, background & verisimilitude

    • Nil Gilman, Associate Chancellor, UC Berkeley
    • Larry Summers, Harvard
  • Cited, as exemplars of extreme good or evil
    • Abraham Lincoln
    • ISIS
  • sharing economy
  • Airbnb
  • Taskrabbit
  • Uber
  • David Plouffe, lobbyist, ex-Obama 2012
  • The Koch Brothers
  • Stop Online Piracy Act (SOPA)
    • was good
    • crude, but
  • Some article, The Economist (uncited)
    the ability to substitute capital for labor (has profound implications)
  • Reagan, Reagan-era
  • John Maynard Keynes
    opined about substituting capital for labor (the 15 hour work week)
  • Martin Luther King
    credited with the quote “asleep at the reolution”
  • Julie Cohen, professor, Georgetown University
    • opined about privacy
    • popularization, summarization
      Why does Privacy Matter?  One Scholar’s Answer; Jathan Sadowski; In The Atlantic; 2013-02-26.
      Teaser: If we want to protect privacy, we should be more clear about why it is import
      Summarized as: <quote>Privacy is not just something we enjoy. It is something that is necessary for us to: develop who we are; form an identity that is not dictated by the social conditions that directly or indirectly influence our thinking, decisions, and behaviors; and decide what type of society we want to live in.</quote>
  • Aldous Huxley
  • Virtual Reality’s Potential Displayed at Game Developers’ Conference; In The New York Times (NYT); 2015-03-06.
  • Nir Eyal; Hooked: How to Build Habit Forming Products; Nir Eyal, via Amazon; 2013-12-30; 156 pages; kindle: $14, paper: $12+SHT.
  • Sundance Courts a New Celebrity Crowd; some cub reporter; In The New York Times (NYT); 2015-02-01.
    tl;dr → Sundance Film Festival, grift, bribes for promotion
  • Thomas Jefferson
  • Plato
  • Vox Media
  • BuzzFeed
  • Argot
  • native advertising
  • brand integration
  • Facebook
  • Artists
    • Bob Dylan
    • George Harrison
    • Martin Scorsese

Via: backfill.

Platform Siphoning: Ad-Avoidance and Media Content | Simon Anderson, Joshua Gans

Simon P. Anderson, Joshua S. Gans; Platform Siphoning: Ad-Avoidance and Media Content; In American Economic Journal: Microeconomics; 2006-04-07 → 2011-03-16; 44 pages; SSRN.  Previously “Tivoed: The Effect of Ad Avoidance Technologies on Content provider Behavior,” Campaigned at the 4th Workshop on Media Economics (Washington, 2006).

tl;dr → since ads are annoying, consumers will use technology to block them; publishers will react with more & crapper content and more & crappier ads; a downward spiral ensues.  Subscriptions won’t work.

Abstract

Content providers rely on advertisers to pay for content. TiVo, remote controls, and popup ad blockers are examples of ad-avoidance technologies that allow consumers to view content without ads, and thereby siphon off the content without paying the ‘price.’ We examine the content provider’s reaction to such technologies, demonstrating that their adoption increases advertising clutter (leading to a potential downward spiral), may reduce total welfare and content quality, and can lead to more mass-market content. We cast doubt on the profitability of using subscriptions to counter the impact of ad- avoidance.

Conclusion

<quote>Platform siphoning benefits those who are most annoyed by ads, and it can enhance their welfare. But it weakens the two-sided business model. The platform’s response is to raise the ad level. This, we stress, is not per se an attempt to recapture the lost revenues, but rather it comes from the revealed preference of those who do not invest in ad avoidance technology: they are revealed to be less sensitive to ad nuisance and so the marginal incentive to raise the ad level is increased. </quote>

Mentions

  • The “proof” is via that symbolic algrebra that they do in upper-division theoretical economics.
  • Focused about TiVo commercial skipping for linear appointment TV.
  • “media” defined as <quote>no marginal costs to the content provider for expanding
    viewership or advertising.</quote>

Argot

  • Ad Avoidance Technology (AAT)
  • Lowest Common Denominator (LCD) Content → you know it when you see it.

Promotions

References

  • Anderson, Simon P. and Stephen Coate (2005), “Market Provision of Broadcasting: A Welfare Analysis,” Review of Economic Studies, 72 (4): 947-972.
  • Anderson, Simon P. and Jean J. Gabszewicz (2006), “The Media and Advertising: A Tale of Two-Sided Markets,” Handbook of the Economics of Art and Culture, eds. Victor Ginsburgh and David Throsby, Elsevier.
  • Anderson, Simon P. and Joshua S. Gans (2009), Platform Siphoning, available at SSRN:
  • Anderson, Simon P. and Damien J. Neven (1989), “Market Equilibrium with Combinable Products,” European Economic Review, 33 (4): 707-719.
  • Armstrong, Mark (2006), “Competition in Two-Sided Markets”, RAND Journal of Economics, 37(3): 668-691.
  • Armstrong, Mark and Helen Weeds (2007), “Public Service Broadcasting in the Digital World,” The Economic Regulation of Broadcasting Markets, eds. Paul Seabright and Jürgen von Hagen, Cambridge University Press: Cambridge.
  • Beebe, Jack (1977) “Institutional Structure and Program Choices in Television Markets,” Quarterly Journal of Economics, 91(1): 15-37.
  • Caillaud, Bernard and Bruno Jullien (2001), “Competing Cybermediaries,” European Economic Review, 45(4), 797-808.
  • Chen, Yongmin and Michael H. Riordan (2008), “Price-increasing competition,” RAND Journal of Economics, 39(4), 1042-1058.
  • Choi, Jay Pil (2006), “Broadcast Competition and Advertising with Free Entry: Subscription vs. Free-to-Air,” Information Economics and Policy, 18(2), 181-196.
  • Crampes, Claude and Bruno Jullien (2009), “Advertising, Competition and Entry in Media Industries,” Journal of Industrial Economics, 57(1), 7-31.
  • Gabszewicz, Jean, Didier Laussel and Nathalie Sonnac (2004), “Programming and Advertising Competition in the Broadcasting Industry,” Journal of Economics and Management Strategy, 13, 657-669.
  • Grabowski, Henry G. and John M. Vernon (1992) “Brand Loyalty, Entry, and Price Competition in Pharmaceuticals after the 1984 Drug Act,” Journal of Law & Economics, 35(2), 331-350.
  • Johnson, Justin P. (2008), “Targeted Advertising and Advertising Avoidance,” mimeo., Cornell.
  • Johnson, Justin P. and David P. Myatt (2006), “On the Simple Economics of Advertising, Marketing and Product Design,” American Economic Review, 96 (3), 756-784.
  • Manjoo, Farhad (2009), Blocked Ads, Clean Conscience, Slate, 2009-05-14.
  • Moriarty, Sandra E. & Shu-Ling Everett (1994), “Commercial Breaks: A Viewing Behavior Study,” Journalism Quarterly, 71 (Summer), 346-355.
  • Myers, Jack (2009), The TiVo Imperative: Education and Entice Viewers to ‘Want to Watch’ Commercials and New TV Series, The Huffington Post, 2009-05-18.
  • Peitz, Martin and Tommaso M. Valletti (2008), “Content and advertising in the media: pay-TV versus free-to-air,” International Journal of Industrial Organization, 26(4), 949 – 965.
  • Rochet, Jean-Charles and Jean Tirole (2006), “Two-Sided Markets: A Progress Report”, RAND Journal of Economics, 37(3), 645-667.
  • Shah, Sunit (2011), “Ad-Skipping and Time-Shifting: A Theoretical Look at the DVR,” mimeo, University of Virginia.
  • Speck, Paul S. and Michael T. Elliott (1997), “Predictors of Advertising Avoidance in Print and Broadcast Media,” Journal of Advertising, 26(3): 61-76.
  • Steinberg, Brian, and Andrew Hampp (2007), “DVR Ad Skipping Happens, but Not Always,” Advertising Age, 2007-05-31.
  • Tag, Joacim (2009), “Paying to Remove Advertisements,” Working Paper, Swedish School of Economics and Business Administration.
  • Weyl, E. Glen (2010), “A Price-Theory of Multi-Sided Markets,” forthcoming, American Economic Review.
  • White, Alex (2008), “Search Engines: Left Side Quality versus Right Side Profits,” mimeo., Toulouse.
  • Wilbur, Kenneth C. (2005), Modeling the Effects of Advertisement-Avoidance Technology on Advertisement-Supported Media: the Case of Digital Video Recorders, Ph.D. dissertation, University of Virginia.
  • Wilbur, Kenneth C. (2008a), “A Two-Sided, Empirical Model of Television Advertising and Viewing Markets,” Marketing Science, 27 (3): 356-378.
  • Wilbur, Kenneth C. (2008b), “How the Digital Video Recorder Changes Traditional Television Advertising,” Journal of Advertising, 37 (1): 143-149.

The 2015 Ad Blocking Report: The Cost of Ad Blocking | PageFair, Adobe

The 2015 Ad Blocking Report: The Cost of Ad Blocking; PageFair with Adobe; 2015-08-09; 17 pages; landing

tl;dr → it’s everywhere, it’s bad, really really bad.

Promotions

Previously

Actualities

Via: backfill

Do Not Track Compliance Policy, Version v1.0 (dnt-policy-1.0.txt) | Electronic Frontier Foundation (EFF)

Do Not Track Compliance Policy, Version v1.0 as dnt-policy-1.0.txt

 Background

Signatories

  • Ad Block Plus
  • Disconnect
  • DuckDuckGo
  • Electronic Frontier Foundation (EFF)
  • Medium
  • Mixpanel

Mentioned

  • Recommends using an ad blocker (the house brand, Privacy Badger).
  • Login constitutes consent.
  • Unclear – why is  “mobile” different than “desktop” (officework, clamshell, laptop)
    <quote>This policy was designed for desktop browsers interacting with websites. This policy isn’t necessarily appropriate for the mobile environment. Fig.1 [not shown] Medium.com provides DNT users with clear enough guidance at log-in to obtain consent.</quote>
  • Enforcement
    None.  Vague: threat of Federal Trade commission(FTC & state attorneys general (legal) action if actual operations does not match policy declarations.

Referenced

Previously

In archaeological order…

General

Overview

Table is verbatim from the document.

Privacy Concern

Effect of ‘Do Not Track’

Tracking Stops
Filter Bubble / Customized content Stops or de-identifies*
Social Media Widgets (‘like” buttons, etc) Data sent only when clicked
Ads Allowed if privacy compliant
Targeted ads Stops or de-identifies*
Visitor datasets Can only hold if aggregated and de-identified
Webserver logs 10 days (no cookies or unique IDs other than IP)
User-provided information Unchanged
Browsing anonymity Maybe someday.  In the meantime, use Tor.
Protection against unlawful or mass surveillance No

From the section headings of Understanding…;

  • DNT binds First and Third Parties
  • DNT means AND
    •  Do Not Collect
    • And Do Not Retain
  • Except where Required OR
    • Necessary to Complete a Transation
    • With The Clear Consent of the Consumer
  • Not Appropriate for “Mobile”
    unclear why not.

Promotions

Via: backfill

The Mobile Web Sucks, because of advertising

The Series

The compendium, in archaeological order…

Mentions

  • Ben Thompson; Why Web Pages Suck; In His Blog entitled Stratechery; 2015-07-15.
    riffing against: Gruber’s complaint.
  • John Gruber; Safari Content Blocker, Before and After; In His Blog entitled Daring Fireball; 2015-07-08; separately filled.
    Mentions

    • Apple news site iMore
    • Safari Content Blocker system would cause a “reckoning” for publishers b
    • <quote>With Safari Content Blockers, Apple is poised to allow users to fight back. Apple has zeroed in on what we need: not a way to block ads per se, but a way to block obnoxious JavaScript code. A reckoning is coming.</quote>
  • Whereas The Vergepublishes 40 linkbait packages per day
    • It is not possible to do this on multiple proprietary publishing systems.
    • ergo Web publishing in “standard” HTML and “standard” adtech to monetize.
  • The web is too slow & bloated so, therefore
    • Apple News (iMore)
    • Facebook Instant Articles
  • Mobile/Tablet Browser Market Share; At NetMarketShare.
  • There is some chain of reasoning in the middle that induces a causal relationship between performance and ecosystem health:
    • Chain of Reasoning
      • Bad PC software created the opening for The Web
      • Bad Mobile Web created the opening for Mobile Apps
      • Unvoiced: something about opening the way for Officework/Desktop/EXE/Apps (again).
    • Claim: Microsoft is giving away Windows 10 because Windows 10 exe files will “run anywhere” (ahem: write once, run anywhere).
    • <quote>Apps have become nearly irrelevant on desktops because the web experience is close to perfect, while apps are vitally important on phones because the web experience is dismal. Windows 10 looks like it’s going to be a big step forward for Microsoft, but it won’t be able to bridge that gap. I’m not sure anything can.</quote>

Exemplars

  • Apple News (iMore)
  • Facebook Instant Articles
  • Facebook
  • Twitter
  • Web Fonts

Actualities

Unsanctioned Web Tracking | W3C

Unsanctioned Web Tracking, Finding, Technical Architecture Group (TAG), W3C,

This Version:
unsanctioned-tracking-20150717
Latest Version:
unsanctioned-tracking
Latest editor’s draft:
work site
Editor:
Mark Nottingham

Findings

Section 5

<quote>

  • Finds that unsanctioned tracking is actively harmful to the Web, because it is not under the control of users and not transparent.
  • Believes that, because combatting fingerprinting is difficult, new Web specifications should take reasonable measures to avoid adding unneeded fingerprinting surface area. However, added surface area should not be a primary factor in determining whether to add a new feature.
  • Asserts that when a new feature does add fingerprinting surface area, it should be documented as such.
  • Finds that new local storage features and other potential tracking mechanisms should maintain and interoperate with existing user controls.
  • Encourages browser vendors to expose appropriate controls to users who wish to minimize their fingerprinting surface area.
  • Acknowledges that despite best efforts, technical solutions to unsanctioned tracking are not able to completely prevent its use by a determined adversary. Instead, our focus should be on making sure that unsanctioned tracking does not become “normal” on the Web.
  • Encourages policy makers to be aware that unsanctioned tracking may introduce privacy, security and consumer protection concerns within their jurisdiction, and to consider appropriate action.

</quote>

Definitions

Light on the definition of the effect (what is ‘unsanctioned tracking’?).  This seems to be enumerated in Sections 1 & 2 as:

  • unsanctioned web tracking → is the inverse of standards-defined tracking.
  • standards-defined web tracking→ interpreted as
    • Technologies
      • HTML4 State (Cookies)
      • HTML5 Web Storage
    • Acceptable pattern of use
      • Pixels (GET of zero-sized, no-op, documents [images])
      • Consumer-visiblity affordance
      • Consumer-visible opt out signalling.
    • Acceptable product features & business models
      • shopping carts
      • persistent site preferences
      • behavioral advertising
      • [unclear the list is closed or open]

Not Mentioned

  • Advertising Identifiers, e.g. IDFA, GPSAID
  • Geofencing, geo-behavioral identification.

References

Appendix A

[RFC6265]
A. Barth. HTTP State Management Mechanism. 2011-04. Proposed Standard. URL:
[confinement]
Butler W. Lampson. A Note on the Confinement Problem. In Communications of the ACM; Volume 16, Number 10; 1973-10; 5 pages.
[spy-sandbox]
Yossef Oren; Vasileios P. Kemerlis; Simha Sethumadhavan; Angelos D. Keromytis. The Spy in the Sandbox – Practical Cache Attacks in Javascript.; previously filled.
[udhr]
Universal Declaration of Human Rights.
[webstorage]
Ian Hickson. Web Storage (Second Edition). 2015-06-09. W3C Candidate Recommendation.

Inline

Linked within the document; in order of appearance

Related

Rebuttal

This is a straw man, a red herring, a toy argument.  The elements cited are substantially fringe techniques in any case, but that not withstanding.  There is no such category as unsanctioned tracking.  All in-industry tracking&targeting is done under consumer consent, with agreements voluntarily entered-into with full presentment of Notice & the availability of affordance of Choice subject to the stated Terms & Conditions of the owner of the (entertainment) service which being delivered unto the consumer for their enjoyment.  There is no other kind of trak-N-targ except under consumer consent; it simply doesn’t exist, it can’t exist by definition.  Acceptance of the T&C contract is by adhesion and the consumer’s remedy upon inability to accept the T&C is to leave the area [leave the internet].  For fun, here is a publisher who makes this framework very clear: <quote>If you don’t agree to the terms contained in this User Agreement and Privacy Policy, you must immediately exit the Service.</quote>

California Privacy Policy; At Condeé Nast, in force at Ars Technica; 2014-01-02 → 2015-07-17 (present).


Via: backfill

Automated Experiments in Ad Privacy Settings: A Tale of Opacity, Choice and Discrimination | Datta, Tschantz, Datta

Amit Datta, Michael Carl Tschantz, Anupam Datta; Automated Experiments in Ad Privacy Settings: A Tale of Opacity, Choice and Discrimination; In Proceedings of Privacy Enhancing Technologies Symposium (PETS);  2015-04-01; landing.

Abstract

To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

Mentioned

  • Google
  • AdSettings
  • AdFisher
  • Experimental design: blocking (see appendix)

Technology

  • Selenium
  • Python
  • Firefox
  • scikit-learn
  • SciPy

Availability

Rebuttals

Promotions

A 72-hour press cycle; archaeological order…

References

  • R. Mayer, J. C. Mitchell, “Third-party web tracking: Policy and technology,” in Proceedings of the IEEE Symposium on Security and Privacy (SP), 2012, pp. 413–427.
  • B. Ur, P. G. Leon, L. F. Cranor, R. Shay, Y. Wang, “Smart, useful, scary, creepy: Perceptions of online behavioral advertising,” in Proceedings of the Eighth Symposium on Usable Privacy and Security. ACM, 2012, pp. 4:1–4:15.
  • Google, About ads settings, 2014-11-21.
  • Yahoo!, Ad interest manager, 2014-11-21.
  • Microsoft, Microsoft personalized ad preferences, 2014-11-21.
  • Executive Office of the President, Big data: Seizing opportunities, preserving values, 2014.
  • R. Zemel, Y. Wu, K. Swersky, T. Pitassi, C. Dwork, Learning fair representations, in Proceedings of the 30th International Conference on Machine Learning (ICML-13); S. Dasgupta and D. Mcallester (editors), vol. 28. JMLR Workshop and Conference Proceedings, 2013-05, pp. 325–333.
  • Google, Privacy policy, 2014-11-20.
  • F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, “Scikit-learn: Machine learning in Python,” In Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  • E. Jones, T. Oliphant, P. Peterson et al., SciPy: Open source scientific tools for Python, 2001,
  • M. C. Tschantz, A. Datta, A. Datta, J. M. Wing, A methodology for information flow experiments, ArXiv, Tech. Rep. arXiv:1405.2376, 2014.
  • P. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses. Springer, 2005.
  • C. E. Wills and C. Tatar, “Understanding what they do with what they know,” in Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society (WPES), 2012, pp. 13–18.
  • P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, S. Muthukrishnan, “Adscape: Harvesting and analyzing online display ads,” in Proceedings of the 23rd International Conference on World Wide Web (WWW). 2014, pp. 597–608.
  • B. Liu, A. Sheth, U. Weinsberg, J. Chandrashekar, R. Govindan, “AdReveal: Improving transparency into online targeted advertising,” in Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks. ACM, 2013, pp. 12:1–12:7.
  • M. Lécuyer, G. Ducoffe, F. Lan, A. Papancea, T. Petsios, R. Spahn, A. Chaintreau, R. Geambasu, “XRay: Increasing the web’s transparency with differential correlation,” in Proceedings of the USENIX Security Symposium, 2014.
  • S. Englehardt, C. Eubank, P. Zimmerman, D. Reisman, A. Narayanan, Web privacy measurement: Scientific principles, engineering platform, and new results, 2014, 2014-11-22.
  • S. Guha, B. Cheng, P. Francis, “Challenges in measuring online advertising systems,” in Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (IM), 2010, pp. 81–87.
  • R. Balebako, P. Leon, R. Shay, B. Ur, Y. Wang, L. Cranor, “Measuring the effectiveness of privacy tools for limiting behavioral advertising,” in Proceedings of the Web 2.0 Security and Privacy Workshop, 2012.
  • L. Sweeney, “Discrimination in online ad delivery,” In Communications of the ACM, vol. 56, no. 5, pp. 44–54, 2013.
  • R. A. Fisher, The Design of Experiments. Oliver & Boyd, 1935.
  • S. Greenland and J. M. Robins, “Identifiability, exchangeability, and epidemiological confounding,” In International Journal of Epidemiology, vol. 15, no. 3, pp. 413–419, 1986.
  • T. M. Mitchell, Machine Learning. McGraw-Hill, 1997.
  • D. D. Jensen, Induction with randomization testing: Decision-oriented analysis of large data sets, Ph.D. dissertation, Sever Institute of Washington University, 1992.
  • Is popularity in the top sites by category directory based on traffic rank?, Alexa, 2014-11-21.
  • C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
  • S. Holm, “A simple sequentially rejective multiple test procedure,” In Scandinavian Journal of Statistics, vol. 6, no. 2, pp. 65–70, 1979.
  • Google privacy and terms, Google, 2014-11-22.
  • H. Abdi, “Bonferroni and Šidák corrections for multiple comparisons,” in Encyclopedia of Measurement and Statistics, N. J. Salkind, editor. Sage, 2007.
  • D. Hume, A Treatise of Human Nature: Being an Attempt to Introduce the Experimental Method of Reasoning into Moral Subjects, 1738, book III, part I, section I.
  • “On pay gap, millennial women near parity — for now: Despite gains, many see roadblocks ahead,” Pew Research Center’s Social and Demographic Trends Project, 2013.
  • T. Z. Zarsky, “Understanding discrimination in the scored society,” In Washington Law Review, vol. 89, pp. 1375–1412, 2014.
  • R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, C. Dwork, “Learning fair representations,” in Proceedings of the 30th International Conference on Machine Learning, JMLR: W&CP, vol. 28. JMLR.org, 2013, pp. 325–333.
  • Editor, Adwords cost per click rises 26% between 2012 and 2014, Adgooroo, DATE?
  • L. Olejnik, T. Minh-Dung, C. Castelluccia, “Selling off privacy at auction,” in Proceedings of the Network and Distributed System Security Symposium (NDSS). The Internet Society, 2013.
  • C. J. Clopper, E. S. Pearson, “The use of confidence or fiducial limits illustrated in the case of the binomial,” In Biometrika, vol. 26, no. 4, pp. 404–413, 1934.

Via: backfill.

Do Not Track (Documentary), Episodes 1-7

Do Not Track

Episodes contains links to the episodes & popularization pieces on other venues

  • S01E01 : Morning rituals, 2015-04-14.
  • S01E02 : Breaking Ad, 2015-04-14.
  • S01E03 : Like Mining (in German, with English subtitles), 2015-04-26.
  • S01E04 : The spy in my pocket, 2015-05-12.
  • S01E05 : Big Data Inside the Algorithm, 2015-05-26.
  • S01E06 : The Daily Me, 2015-06-09.
  • S01E07 : To change the future, click here, 2015-06-15.

tl;dr → facile, shrill, handwringy.  Very media-arts-y. Ominous music to set the mood. No new information. Very, very slow-paced.

Mentions

  • disconnect.me
  • Depicts that Yahoo only has two trackers on it
    • Yahoo
    • comScore
  • DoubleClick
    Something about a legal precedent in the early oughties that established that tracking was not wiretapping; it was (something about) being with your friends.  No citation.  Julia Angwin voices the statement.
  • Illuminatus
    Something about eCommerce preferences via your social media account (your Facebook account).
  • UDID
    Still spoken of in Episode 4 as if it were current & available.
  • Big Data
    • is very big.
    • is very bad.
  • There be dragons.
  • Collaborative Filtering
    • is bad
    • causes polarlization (polarization is bad)
    • Facebook uses Collaborative Filtering
      ∴ Facebook is bad
    • Twitter uses Collaborative Filtering
      ∴Twitter is bad
  • Depicted, but not discussed.
    • Content farms in general
    • Demand Media
    • Associated Content (Yahoo)
    • Buzzfeed

Claims

  • … that if consumers all, each, paid some … it would all be wonderful & ad-free.
  • ARPU
    • Facebook → $9/year
    • Google →$55/year
  • Ethan Zuckerman, MIT Media Lab
    Claims he invented the popup ad to ensure Ford Motor Company did not get car ads on anal sex sites (or a story substantially similar to this line of causality).  He claims he invented the popup ad. And: Ethan Zuckerman, Who Invented Pop-Up Ads Says ‘I’m Sorry’; In Forbes; 2014-08-15.

Referenced

Appearing

the activists…

  • Danah Boyd, Data & Society Institute.
  • Nathan Frietas, The Guardian Project.
  • Harlo Holmes, The Guardian Project
  • Ethan Zuckerman, MIT Media Lab
  • Julia Angwin, self; ex-Wall Street Journal (WSJ)
  • Michal Kosinski, Stanford University
  • Jeffrey Stewart, CEO, Leddo
  • Natalie Blanchard, IBM, exemplar of a depressant
  • Marcus Behdahl?  Some news organism, in EU.
  • Mathieu Desjardins, WHERE?
  • Cory Doctorow, self.
  • Kate Crawford, Microsoft.
  • Tyler Virgen, Spurrious Correlations
  • Alicia Garza, #BlackLivesMatter.
  • Emily Bell, Tow Center, School of Journalism, Columbia University; ex-Guardian
  • Gilad Lotan, Chief Data Scientist, Betaworks.
  • Someone, Episode 7.

Tracking Protection in Firefox for Privacy and Performance | Kontaxis, Chew

Georgios Kontaxis (Columbia), Monica Chew (Mozilla); Tracking Protection in Firefox for Privacy and Performance; In Proceedings of the Web 2.0 Security and Privacy (W2SP); 2015-05-23; 4 pages; copy, slides (18 slides).

Abstract

We present Tracking Protection in the Mozilla Firefox web browser. Tracking Protection is a new privacy technology to mitigate invasive tracking of users’ online activity by blocking requests to tracking domains. We evaluate our approach and demonstrate a 67.5% reduction in the number of HTTP cookies set during a crawl of the Alexa top 200 news sites. Since Firefox does not download and render content from tracking domains, Tracking Protection also enjoys performance benefits of a 44% median reduction in page load time and 39% reduction in data usage in the Alexa top 200 news sites.

Mentions

  • Mozilla Firefox
  • Configuration
    • about:config
    • privacy.trackingprotection.enabled=true
  • Release
    • Firefox Nightly
    • Firefox 35
    • Not committed for any production release?
  • Development
    • 1029886tracking bug for tracking protection
  • Architecture
    • curated blocklist
    • Disconnect’s list (not EasyList)
    • (Google) SafeBrowsing API
  • Features
    • Cookie Blocking
    • Beacon Blocking
  • Justification
    • Performance (page latency reduction).
    • Sotto voce, surveillance blocking.
    • Sotto voce, ad blocking.
  • Threat Model
    • <quote cite=”ref” page=”2″>Our adversary is a powerful billion-dollar online advertising and social networking industry</quote>
  • trackingprotectionfirefoxat some github.
  • Performance claims
    • some telemetry
    • some simulation

Similar

Somehow solving similar problems.

Actualities

Promotions

Archaeological order…

Footnoted

References

Opinion

Wandering, moot, through the naïvete of the chain of reasoning here, flow with it.

Claim

Authors = <quote cite=”ref” page=”4″>

Finally, browser makers bear tremendous responsibility in mediating conflicts between privacy interests of users and the advertising and publishing industries. Tracking Protection for Firefox is off by default and hidden in advanced settings. We call upon Mozilla, Microsoft, and other browser makers to make tracking protection universally available and easy to use. Only then will the balance of power shift towards interests of the people instead of industry.

</quote>

Rebuttal

Greybeard = <moot>

Browser makers can’t have it both ways here.  They can’t be “common carriers” who make net-neutral and nework-neutral consumer premises equipment (CPE) as pure-play suppliers the media trade and also be the arbiters of the rights, rules and procedures of that industry without also entering that industry as a primary; i.e. as a publisher which owns a venue and manages an audience, which, as busking, is a fine and honorable vocation with a long and storied tradition dating back to the earliest ages.  Indeed Firefox Sponsored Tiles.

Hiding such intervention capability in the “advanced settings” doesn’t ameliorate the conceptual error here. The terms of the trade have always and ever been between the publisher and the advertiser. The consumer (which is you, dear reader), as a catalyst of the relationship, is party to this activity only insofar is the terms of the publisher-advertiser business arrangement specify that the publisher is able to deliver any quantifiable action, generally, quantifiable attention, of the consumer (which, to remind, is you, dear reader) to the advertiser under the terms of their bilateral deal (common commercial terms being: CPM, CPC, CPA, etc.).  The consumer’s consent being entailed by virtue of having received media from the publisher in the first instance.

As for your part of this, you are a consumer, and only that.  As the appelation implies, you don’t own the creative product that you’re enjoying, you never did, you never will. Your rights are limited to personal experience under the stated terms.  Otherwise, by convention, broader allowances would had to have been granted to you in an expression, an explicit writ. Your activities with regard to blocking publishers trading with advertisers in order to petition them to change their business practices as you experience them is a project that is, at best, fraught with contradictions and complications. To want to change the legal framework of creative product ownership & delivery is a tall order and would necessarily have implications in other areas of the media business.  The law is pretty clear on the countervailing point.  Namely, that the publisher owns the media, as they created it. They are purveying it under terms set forth. The media is licensed to you, and performed for you, even when on equipment that you own, for the sole purpose of your private enjoyment as an individual.  During your experience of the work, you do not receive any other rights, such as the right of derivation, summarization, retransmission, republication, public performance, etc.  These conditions adhere to you by your presence in the experience as a consumer unit. You are necessarily subject ot the Terms & Conditions set forth at the time the media was administered to you.  Indeed the whole foundation of the Creative Commons and Open Source licensing is centered upon this point.

</moot>

Counter-Rebuttal

Activist = <moot2>

Yet “we” build, “we” own & “we” operate the CPE. These HTML5-JS-CSS3 browser media-players are “ours.”  We are the web!  Unlike print, OTA TV or radio media where the players are locked down. We build CPE; we block as we like. This cannot be stopped.

</moot2>

Counter2-Rebuttal

Publisher = We parry and invoke EME, CDM, DRM & block you with DMCA. Like we do with video. QED.

Via: backfill.

MAdScope: Characterizing Mobile In-App Targeted Ads | Nath

Suman Nath; MAdScope: Characterizing Mobile In-App Targeted Ads; In International Conference on Mobile Systems, Applications, and Services (MobiSys); 2015-05; 15 pages; landing (Microsoft).

tl;dr => only DoubleClick uses targeting; DoubleClick is 36% of ads served; Otherwise targeting is unused; targeting does not work (it does not deliver different creatives at the consumer, outside of DoubleClick).

Abstract

Advertising is the primary source of revenue for many mobile apps. One important goal of the ad delivery process is targeting users, based on criteria like users’ geolocation, context, demographics, long-term behavior, etc. In this paper we report an in-depth study that broadly characterizes what targeting information mobile apps send to ad networks and how effectively, if at all, ad networks utilize the information for targeting users. Our study is based on a novel tool, called MAdScope, that can

  1. quickly harvest ads from a large collection of apps,
  2. systematically probe an ad network to characterize its targeting mechanism, and
  3. emulate user profiles of specific preferences and interests to study behavioral targeting.

Our analysis of 500K ad requests from 150K Android apps and 101 ad networks indicates that apps do not yet exploit the full potential of targeting: even though ad controls provide APIs to send a lot of information to ad networks, much key targeting information is optional and is often not provided by app developers. We also use MAd Scope to systematically probe top 10 in-app ad networks to harvest over 1 million ads and find that while targeting is used by many of the top networks, there remain many instances where targeting information or behavioral profile does not have a statistically significant impact on how ads are chosen. We also contrast our findings with a recent study of targeted in-browser ads.

Mentions

  • MAdScope
  • MAdFraud
  • Monkey
  • PUMA

Perspective

The paper is to be viewed as a reprise of Barford et al.‘s AdScape which was focused on webware (in-browser) advertising, whereas the focus here is adware (in-app) advertising.  The claim in Barford was that “targeting works, more or less” by the definition of “it can be detected.”

P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, S. Muthukrishnan. AdScape: Harvesting and Analyzing Online Display Ads. In Proceedings of the International Conference on the World Wide Web (WWW); 2014; 11 pages. arXiv:1407.0788

Data Set

  1. Ad Calls
    • 500K ad calls
    • 150K adware (Android)
  2. Creatives Sampled
    • 10 Ad Newworks (“top ten”)
    • 1M Creatives Sampled

Population

  1. adsmogo.com
  2. airpush.com
  3. applovin.com
  4. appsgeyser.com
  5. doubleclick.net
  6. mobclicx.com
  7. mobfox.com
  8. mopublcom
  9. nend.net
  10. wapx.cn

Claims

<quote>targeting is limited in in-app ads: even though ad controls provide APIs to send a lot of information to ad networks, much key targeting information is optional and is often not provided by apps.</quote>

<quote>while targeting is used by many of the top networks, there remain many instances where targeting information or behavioral profile does not have a statistically significant impact on how ads are chosen.</quote>

  • Location => substantially unused
    • 3/7 ad networks collect but do not use
    • 1/7 (applovin.com) uses “marginally”
    • 3/7 do not use at all
  • Keywords => substantially unused
    • 3/7 accept keywords
      • 2/3 (2/7) actually use the keywords supplied
  • Device => collected but unused
    • 7/10 collect device information
      • “most” (4/7) ignore
      • 3/7 use it (do not ignore it)
  • Demographics & “is a child”
    • 1/10 collects
      • “marginal use” (whatever that means)
  • App Name / App ID
    • 8/10 collected
      • 4/8 use it

<quote page=”13″>We found that doubleclick.net, used by 36% ad requests in our datasets, differentiates between profiles in a statistically significant way while serving ads. We did not observe any statistically significant impact of profiles for other networks, but that could well be due to the limitations mentioned in Section 4.2. For doubleclick.net, the impact of profiles is significant—we found that 80% of the ads were targeted towards specific profiles (e.g., distributed non-uniformly).</quote>

Actualities

Referenced

  1. P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, S. Muthukrishnan. AdScape: Harvesting and Analyzing Online Display Ads. In Proceedings of the International Conference on the World Wide Web (WWW), 2014. 11 pages.
  2. R. Bhoraskar, S. Han, J. Jeon, T. Azim, S. Chen, J. Jung, S. Nath, R. Wang, D. Wetherall. Brahmastra: Driving Apps to Test the Security of Third-Party Components. In Proceedings of USENIX Security, 2014.
  3. C. Castelluccia, M. A. Kaafar, M.D. Tran, Betrayed by Your Ads! Reconstructing User Profiles from Targeted Ads; In Proceedings of the 12th International Conference on Privacy Enhancing Technologies (PETS). 2012. 17 pages.
  4. X. Chen, A. Jindal, Y. C. Hu. How Much Energy Can We Save From Prefetching Ads?: Energy Drain Analysis of Top 100 Apps. In Proceedings of the Workshop on Power-Aware Computing and Systems (HotPower). ACM, 2013.
  5. J. Crussell, R. Stevens, H. Chen. MAdFraud: Investigating Ad Fraud in Android Applications. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys) ACM, 2014. landing., dataset.
  6. T. Danny. Flurry releases newest app use stats. In Some Blog.
  7. D. S. Evans. The Online Advertising Industry: Economics, Evolution, and Privacy. In Journal of Economic Perspectives. Volume 23, Number 3. 2009 (Summer). pages 27-60 (26 pages). landing, copy.
  8. A. Farahat, M. C. Bailey. How Effective is Targeted Advertising? In Proceedings of the International Conference on the World Wide Web (WWW). 2012-04-16. 10 pages. slides (43 slides)
  9. GameHouse. Mobile advertising statistics—5 big trends you need to know!. In Some Blog.
  10. M. Gandhi, M. Jakobsson, J. Ratkiewicz. Badvertisements: Stealthy Click-Fraud with Unwitting Accessories. In Online Fraud, Part I. In Journal of Digital Forensic Practice, 1(2), 2006.
  11. A. Goldfarb, C. Tucker. Online Display Advertising: Targeting and Obtrusiveness. In Marketing Science, 2010. paywall.
  12. M. Grace, W. Zhou, X. Jiang, A. Sadeghi. Unsafe Exposure Analysis of Mobile In-App Advertisements. In Conference on Security and Privacy in Wireless and Mobile Networks (WiSEC), 2012.
  13. S. Guha, B. Cheng, P. Francis. Challenges in Measuring Online Advertising Systems. In Proceedings of the ACM SIGCOMM Internet Measurement Conference, 2010-11-01. 7 pages. html.
  14. S. Hao, B. Liu, S. Nath, W. G. Halfond, R. Govindan. PUMA: Programmable UI-Automation for Large Scale Dynamic Analysis of Mobile Apps. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys) ACM. 2014.
  15. A. J. Khan, K. Jayarajah, D. Han, A. Misra, R. Balan, S. Seshan. CAMEO: A Middleware for Mobile Advertisement Delivery. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys) ACM. 2013. slides.
  16. M. Lécuyer, G. Ducoffe, F. Lan, A. Papancea, T. Petsios, R. Spahn, A. Chaintreau, R. Geambasu. Xray: Enhancing the Web’s Transparency with Differential Correlation. In Proceedings of USENIX Security, 2014. previously noted.
  17. I. Leontiadis, C. Efstratiou, M. Picone, C. Mascolo. Don’t Kill My Ads!: Balancing Privacy in an Ad-Supported Mobile Application Market. In Proceedings of the Workshop on Mobile Computing Systems & Applications, ACM. 2012. page 2 (6 page).
  18. B. Liu, S. Nath, R. Govindan, J. Liu. DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2014.
  19. P. Mohan, S. Nath, O. Riva. Prefetching Mobile Ads: can Advertising Systems Afford It? In Proceedings of the ACM European Conference on Computer Systems (EuroSys), 2013.
  20. P. Pearce, A. Felt, G. Nunez, D. Wagner. AdDroid: Privilege Separation for Applications and Advertisers in Android. In Proceedings of the Symposium on Information, Computer and Communications Security, 2012.
  21. T. Peterson. Google tests way to track consumers from mobile browsers to the apps they use.; In Ad Age
  22. L. Ravindranath, S. Nath, J. Padhye, H. Balakrishnan. Automatic and Scalable Fault Detection for Mobile Applications. In Proceedings of the Workshop on Mobile Computing Systems & Applications (MobiSys), ACM., 2014.
  23. Nota Bene: Reference 23 is incorrect in the published work. There is another work of interest by that title with a different author set and venue.
  24. S. Shekhar, M. Dietz, D. S. Wallach. Adsplit: Separating Smartphone Advertising from Applications. In Proceedings of the USENIX Security Symposium, 2012.
  25. A. N. Srivastava, M. Sahami. Text Mining: Classification, Clustering, and Applications. CRC Press, 2010. Amazon: $72+SHT.
  26. R. Stevens, C. Gibler, J. Crussell, J. Erickson, H. Chen. Investigating User Privacy in the Android Ad Libraries. In Proceedings of the IEEE Conference on Mobile Security Technologies (MoST), 2012.
  27. I. Ullah, R. Boreli, D. Kaafar, S. Kanhere. Characterising User Targeting for In-App Mobile Ads. In Proceedings of the INFOCOM International Workshop on Security and Privacy in Big Data (BigSecurity), 2014. paywall, landing.
  28. N. Vallina-Rodriguez, J. Shah, A. Finamore, Y. Grunenberger, H. Haddadi, K. Papagiannaki, J. Crowcroft. Breaking For Commercials: Characterizing Mobile Advertising. In Proceedings of the ACM SIGCOMM Internet Measurement Conference, 2012. previously noted.
  29. W3C. Same Origin Policy.
  30. J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, Z. Chen. How Much Can Behavioral Targeting Help Online Advertising? In Proceedings of the International Conference on the World Wide Web (WWW), 2009.

Via: backfill

Taming the Android AppStore: Lightweight Characterization of Android Applications | Vigneri, Chandrashekar, Pefkianakis, Heen

Luigi Vigneri, Jaideep Chandrashekar, Ioannis Pefkianakis, Olivier Heen; Taming the Android AppStore: Lightweight Characterization of Android Applications; Research Report RR-15-305; EURECOM, Sophia-Antipolis FR; 2015-04-27; 20 pages; arXiv:1504.06093.

tl;dr => apps run gobs of beacons; 20-33% is ad-related.   Yet nothing to see here.

reminder => the consent event of the consumer pertaining to this treatment occurred when the app was purchased (requested for download). Contractually & regulatory framing: the consumer was notified, they consented, and the experience was delivered as was transparently indicated in the terms of service. The consumer asked for it.

Abstract

There are over 1.2 million applications on the Google Play store today with a large number of competing applications for any given use or function. This creates challenges for users in selecting the right application. Moreover, some of the applications being of dubious origin, there are no mechanisms for users to understand who the applications are talking to, and to what extent. In our work, we first develop a lightweight characterization methodology that can automatically extract descriptions of application network behavior, and apply this to a large selection of applications from the Google App Store. We find several instances of overly aggressive communication with tracking websites, of excessive communication with ad related sites, and of communication with sites previously associated with malware activity. Our results underscore the need for a tool to provide users more visibility into the communication of apps installed on their mobile devices. To this end, we develop an Android application to do just this; our application monitors outgoing traffic, associates it with particular applications, and then identifies destinations in particular categories that we believe suspicious or else important to reveal to the end-user.

Table of Contents

  • Introduction
  • Background
  • Related Work
  • Dataset
    • Application Selection
    • Application Execution
    • URL Analysis
  • Application Destination Characterization
    • Detailed Apps Characterization
    • Advertising Intensity
    • Tracking Intensity
    • App Suspiciousness
    • App Category Behavior
    • Application Description
  • Conclusion

Mentions

  • No Such App (NSA)
    • Their app is called NSA, ’cause that’s cool.
    • Not available in Google PlayStore
    • Direct link https://db.tt/Cx8fB5Xz (who knows what that is)
    • SandroProxy for MITM of the SSL
  • Traffic Analysis
    • HTTP only
    • HTTPS is not analyzed
  • Endpoint Characterization
    • It’s an Ad Related Endpoint if it has come to the attention of Ad Block Plus’ EasyList.
    • It’s a Malevolent Endpoint if it has come to the attention of Webutation, VirusTotal or Google Safe Browsing.
  • Data usage
    • 20-33% of data usage is categorized as “ad related”; Table 11.

Claimed

The percentage of apps in from the Google Play Store which contact these domains.  Recall though that the act of downloading the app onto your box was the consent event that makes this all copacetic (i.e. formally, you asked to have this happen to you)..

Domain Contacts
doubleclick.net 41.50%
google.com 35.80%
gstatic.com 35.40%
admob.com 26.60%
googlesyndication.com 23.80%
google-analytics.com 17.20%
ggpht.com 17.00%
fonts.googleapis.com 13.90%
googleusercontent.com 12.80%
samsungvideohub.com 8.80%
ajax.googleapis.com 5.80%
flurry.com 5.60%
nend.net 5.10%
xiti.com 4.80%
facebook.com 4.80%
youtube.com 4.10%
scorecardresearch.com 4.10%
inmobi.com 3.70%
ytimg.com 3.40%
twitter.com 3.30%

The percentage of data use in apps. Table 11 (reordered & re-presented to highlight the most-common categories.  The “IT” category is a default “other” type of category meant to include any bookkeeping traffic that wasn’t otherwise categorizable.

Ads “IT” News Search Social Dynamic
LIBRARIES/DEMO 26.20% 31.20% 6.60% 19.50% 0.00% 0.90%
LIFESTYLE 23.20% 24.30% 4.10% 15.10% 4.60% 7.10%
BUSINESS 17.00% 31.50% 0.60% 13.90% 9.10% 7.90%
ENTERTAINMENT 20.10% 26.20% 3.70% 10.70% 2.90% 7.80%
MEDIA/VIDEO 26.00% 25.10% 5.20% 10.30% 5.10% 6.00%
MEDICAL 29.60% 27.40% 5.90% 9.10% 3.20% 8.10%
GAMES 30.10% 29.10% 2.80% 9.80% 0.30% 6.40%
BOOKS/REFERENCE 29.80% 24.20% 5.80% 13.20% 2.50% 6.30%
MUSIC/AUDIO 21.50% 24.00% 3.20% 9.70% 5.20% 8.80%
TRANSPORTATION 24.30% 27.20% 3.80% 17.00% 0.40% 5.10%
SHOPPING 10.90% 25.80% 2.20% 8.20% 9.50% 8.70%
FINANCE 21.20% 31.40% 2.20% 8.80% 5.40% 6.60%
COMICS 31.90% 20.10% 5.40% 13.20% 3.90% 4.40%
PHOTOGRAPHY 30.50% 20.10% 4.90% 15.20% 0.90% 4.90%
WEATHER 26.80% 25.00% 9.10% 14.40% 3.00% 4.80%
PERSONALIZATION 21.00% 28.40% 6.10% 17.30% 0.40% 8.20%
HEALTH/FITNESS 27.80% 25.80% 3.60% 15.20% 4.90% 5.70%
PRODUCTIVITY 27.00% 27.80% 5.70% 13.00% 3.00% 9.60%
COMMUNICATION 28.00% 25.30% 6.10% 17.00% 2.60% 3.50%
TRAVEL/LOCAL 20.30% 21.30% 4.00% 19.50% 3.50% 2.90%
SPORTS 18.30% 24.30% 3.00% 11.40% 9.90% 5.40%
SOCIAL 16.70% 30.10% 3.70% 14.50% 5.20% 4.80%
EDUCATION 32.60% 23.10% 4.10% 17.10% 3.50% 3.80%
TOOLS 33.60% 27.60% 7.00% 14.50% 1.90% 1.90%
NEWS/MAGAZINES 19.50% 30.60% 8.40% 10.30% 11.20% 3.00%

Via: backfill

Analysis of OpenX-Publishers Cooperation | Olejnik, Castelluccia

Lukasz Olejnik, Claude Castelluccia; Analysis of OpenX-Publishers Cooperation; In Proceedings of Hot Privacy Enhancing Technologies Symposium (HotPETS); 2014-07-18; 10 pages.

  • Understood as “Analysis of Cooperation between OpenX and Publishers”
  • University researcher discovers the First Party Exchange,.exposé follows.

Abstract

Real-Time Bidding is a protocol enabling the serving of advertisements. It involves Ad Exchanges, bidders and publishers. In this note, we report the findings of cooperation between OpenX Ad Exchange and selected publishers. The setting has potentially important implications for Web users privacy and security. For example, Web browser mechanisms responsible for blocking third-party cookies are rendered ineffective.

Mentions