Networks of Control | Cracked Labs

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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.

There are now 229 unicorn startups, with $175B in funding and $1.3T valuation | VentureBeat


There are now 229 unicorn startups, with $175B in funding and $1.3T valuation; ; In VentureBeat; 2016-01-18.

tl;dr → VentureBeat has expertise in market research compendia; the promoted pamphlet exhibits such; landinghires.



Listings

Categorized

As organized in the infographic.

Enterprise

  • Applications
    • Customer Relationship Management (CRM)
      • Apttus
      • InsideSales.com
      • Medallia
      • Zeta Interactive
    • Finance & Accounting
      • Coupa
      • Xero
      • Zuora
    • Human Resource Management (HR)
      • Gusto
      • Workday
      • Zenefits
    • Marketing & eCommerce
      • AdKnowledge
      • AppNexus
      • Blippar
      • Deem
      • Hootsuite
      • InMobi
      • IronSource
      • Marketo
      • MediaMath
      • Qualtrics
      • Shopify
      • Sprinklr
      • Surveymonkey
  • Infrastructure
    • Analytics (Big Data & Business Intelligence)
      • Cloudera
      • Domo
      • Hortonworks
      • MarkLogic
      • MongoDB
      • Mu Sigma
      • MuleSoft
      • New Relic
      • Palantir
    • Cloud
      • Actiflo
      • AppDirect
      • AppDynamics
      • CloudFlare
      • Docker
      • Nutanix
      • Simplivity
    • Content Management & Collaboration
      • Atlassian Software Systems
      • Automattic
      • Box
      • DocuSign
      • Dropbox
      • Evernote
      • GitHub
      • Slack
      • Yammer
    • Mobile
      • Good Technology
      • Meitu, Inc.
      • Wandoujia
      • Yello Mobile
    • Networking
      • Cisco Meraki
      • Nicra
      • Twilio
    • Security
      • AVAST Software as.
      • Avant
      • Illumio
      • Lookout
      • Okta
      • Palo Alto Networks
      • Tanium
      • Zscaler
    • Storage
      • Fusion-io
      • Infinidat
      • Nimble Storage
      • Pure Storage
      • Tintri

Industries

  • Cleantech
    • Betterplace
    • Bloom Energy
    • Sapphire Energy
    • Sunrun
  • Fintech
    • Insurance
      • ZhongAn
    • Investment
      • Credit Karma
      • Hanhua Financial
    • Lending
      • China Rapid Finance
      • Funding Circle
      • Jimubox
      • Kabbage
      • Lending Club
      • Lufax
      • Prosper
      • SoFi
      • TransferWise
    • Payments
      • Adyen
      • Klarna
      • Mozido
      • Powa
      • Square
      • Stripe
  • Healthcare & BioTech
    • Intarcia Therapeutics
    • Moderna Therapeutics
    • NantHealth
    • Oscar
    • Proteus Digital Health
    • Stemcentrx
    • Theranos
    • ZocDoc
  • Internet of Things (IoT)
    • Dji
    • Fitbit
    • Jasper Technologies
    • Jawbone
    • Mobileye
    • Nest
  • Other
    • AUTO1
    • Fisker Automotive
    • Njoy
    • Sogou
    • SpaceX
    • WiFi Master Key

Consumer

  • Online Media
    • AVITO.ru
    • BuzzFeed
    • Panshi
    • Rocket Internet
    • Taboola
    • Vox Media
  • Electronics (Consumer Electronics)
    • GoPro
    • Magic Leap
    • Meizu
    • Oculus VR
    • Xiaomi
  • Games & Entertainment
    • FanDuel
    • Kabam
    • Legenary Pictures
    • Machine Zone
    • Razer
    • Vice Media
    • Zynga
  • Retail
    • Coupons, Bargains. Loyalty
      • Coupang
      • Fanil
      • Groupon
      • LaShou
      • LivingSocial
      • Meituan
      • Quotient Technology
    • Home Furnishing
      • Fab.com
      • Houzz
      • Home24
      • Wayfair
    • Marketplaces
      • Alibaba
      • Auction.com
      • Etsy
      • JD.com
      • Snapdeal
      • 58 Daojia
    • Shopping
      • Mobile Shopping
        • Koudai Gouwu
        • One97 Communications
      • Non-Mobile (Laptop/Officework/Desktop) Shopping
        • BelBel
        • Dianping
        • Fanatics
        • Farfetch
        • Flipkart
        • Gilt Groupe Incorporated
        • Global Fashion Group
        • JustFab
        • Lazada
        • Mogujie
        • NONAME LOGO (magenta/purple, with a ‘J’)
        • Trendy International Group
        • VANCL
        • Wish
        • Zalando
        • Zulily
    • Wellness
      • Honest Company
      • Warby Parker
  • Services (Services to Consumers)
    • Audio
      • Beats Electronics
      • Shazam
      • Spotify
    • Education
      • Lynda.com
      • Pluralsight
      • Renaissance Learning
      • Udacity
    • Messaging
      • Kik
      • Tango
      • WhatsApp (of Facebook)
    • Sharing (The Sharing Economy)
      • Airbnb
      • BlaBlaCar
      • Blue Apron
      • Delivery Hero
      • Didi Chuxing
      • Ele.me
      • GrabTaxi
      • HelloFresh
      • HomeAway
      • Instacart
      • Kuaidi Dache
      • Lwjw
      • Lyft
      • Ola
      • Quickr
      • Thumbtack
      • Tujla
      • Uber
      • Wework
      • Yidao Yongche
      • YouTube
    • Social (Networking)
      • Instagram (of Facebook)
      • Facebook
      • Lamabang
      • LinkedIn
      • Nextdoor
      • Pinterest
      • Snapchat
      • Tumblr (of Yahoo)
      • Twitter
    • Other
      • Eventbrite
      • Waze (of Google)

Alphabetical

  • 58 Daojia
  • AUTO1
  • AVAST Software as.
  • AVITO.ru
  • Actiflo
  • AdKnowledge
  • Adyen
  • Airbnb
  • Alibaba
  • AppDirect
  • AppDynamics
  • AppNexus
  • Apttus
  • Atlassian Software Systems
  • Auction.com
  • Automattic
  • Avant
  • Beats Electronics
  • BelBel
  • Betterplace
  • BlaBlaCar
  • Blippar
  • Bloom Energy
  • Blue Apron
  • Box
  • BuzzFeed
  • China Rapid Finance
  • Cisco Meraki
  • CloudFlare
  • Cloudera
  • Coupa
  • Coupang
  • Credit Karma
  • Deem
  • Delivery Hero
  • Dianping
  • Didi Chuxing
  • Dji
  • Docker
  • DocuSign
  • Domo
  • Dropbox
  • Ele.me
  • Etsy
  • Eventbrite
  • Evernote
  • Fab.com
  • Facebook
  • FanDuel
  • Fanatics
  • Fanil
  • Farfetch
  • Fisker Automotive
  • Fitbit
  • Flipkart
  • Funding Circle
  • Fusion-io
  • Gilt Groupe Incorporated
  • GitHub
  • Global Fashion Group
  • GoPro
  • Good Technology
  • GrabTaxi
  • Groupon
  • Gusto
  • Hanhua Financial
  • HelloFresh
  • Home24
  • HomeAway
  • Honest Company
  • Hootsuite
  • Hortonworks
  • Houzz
  • Illumio
  • InMobi
  • Infinidat
  • InsideSales.com
  • Instacart
  • Instagram (of Facebook)
  • Intarcia Therapeutics
  • IronSource
  • JD.com
  • Jasper Technologies
  • Jawbone
  • Jimubox
  • JustFab
  • Kabam
  • Kabbage
  • Kik
  • Klarna
  • Koudai Gouwu
  • Kuaidi Dache
  • LaShou
  • Lamabang
  • Lazada
  • Legenary Pictures
  • Lending Club
  • LinkedIn
  • LivingSocial
  • Lookout
  • Lufax
  • Lwjw
  • Lyft
  • Lynda.com
  • Machine Zone
  • Magic Leap
  • MarkLogic
  • Marketo
  • Medallia
  • MediaMath
  • Meitu, Inc.
  • Meituan
  • Meizu
  • Mobileye
  • Moderna Therapeutics
  • Mogujie
  • MongoDB
  • Mozido
  • Mu Sigma
  • MuleSoft
  • NONAME LOGO (magenta/purple, with a ‘J’)
  • NantHealth
  • Nest
  • New Relic
  • Nextdoor
  • Nicra
  • Nimble Storage
  • Njoy
  • Nutanix
  • Oculus VR
  • Okta
  • Ola
  • One97 Communications
  • Oscar
  • Palantir
  • Palo Alto Networks
  • Panshi
  • Pinterest
  • Pluralsight
  • Powa
  • Prosper
  • Proteus Digital Health
  • Pure Storage
  • Qualtrics
  • Quickr
  • Quotient Technology
  • Razer
  • Renaissance Learning
  • Rocket Internet
  • Sapphire Energy
  • Shazam
  • Shopify
  • Simplivity
  • Slack
  • Snapchat
  • Snapdeal
  • SoFi
  • Sogou
  • SpaceX
  • Spotify
  • Sprinklr
  • Square
  • Stemcentrx
  • Stripe
  • Sunrun
  • Surveymonkey
  • Taboola
  • Tango
  • Tanium
  • Theranos
  • Thumbtack
  • Tintri
  • TransferWise
  • Trendy International Group
  • Tujla
  • Tumblr (of Yahoo)
  • Twilio
  • Twitter
  • Uber
  • Udacity
  • VANCL
  • Vice Media
  • Vox Media
  • Wandoujia
  • Warby Parker
  • Wayfair
  • Waze (of Google)
  • Wework
  • WhatsApp (of Facebook)
  • WiFi Master Key
  • Wish
  • Workday
  • Xero
  • Xiaomi
  • Yammer
  • Yello Mobile
  • Yidao Yongche
  • YouTube (of Google)
  • Zalando
  • Zenefits
  • Zeta Interactive
  • ZhongAn
  • ZocDoc
  • Zscaler
  • Zulily
  • Zuora
  • Zynga

Investigating User Privacy in Android Ad Libraries | Stevens, Gibler, Crussell, Erickson, Chen

Ryan Stevens, Clint Gibler, Jon Crussell, Jeremy Erickson, Hao Chen; Investigating User Privacy in Android Ad Libraries; In Proceedings of MOST (MOST); 2012; 10 pages.

Abstract

Recent years have witnessed incredible growth in the popularity and prevalence of smart phones. A flourishing mobile application market has evolved to provide users with additional functionality such as interacting with social networks, games, and more. Mobile applications may have a direct purchasing cost or be free but ad-supported. Unlike in-browser ads, the privacy implications of ads in Android applications has not been thoroughly explored. We start by comparing the similarities and differences of in-browser ads and in-app ads. We examine the effect on user privacy of thirteen popular Android ad providers by reviewing their use of permissions. Worryingly, several ad libraries checked for permissions beyond the required and optional ones listed in their documentation, including dangerous permissions like CAMERA , WRITE CALENDAR and WRITE CONTACTS . Further, we discover the insecure use of Android’s JavaScript extension mechanism in several ad libraries. We identify fields in ad requests for private user information and confirm their presence in network data obtained from a tier-1 network provider. We also show that users can be tracked by a network sniffer across ad providers and by an ad provider across applications. Finally, we discuss several possible solutions to the privacy issues identified above.

Referenced

Claims

  • Mobclix: exfiltrate and/or modify the user’s calendar and contacts, exfiltrate user’s audio and image files, and turn on/off the camera LED.
  • Greystripe: get and/or set user’s cookies.
  • mOcean: send SMS and email messages, start phone calls, add calendar entries, get location, make arbitrary network requests.
  • Inmobi: send SMS and email messages, start phone calls, and modify the users calendar.

Breaking for Commercials: Characterizing Mobile Advertising | Vallina-Rodriguez, Shah, Finamore, Grunenberger, Haddadi, Papagiannaki, Crowcroft

Narseo Vallina-Rodriguez, Jay Shah, Alessandro Finamore, Yan Grunenberger, Hamed Haddadi, Konstantina Papagiannaki, John Crowcroft; Breaking for Commercials: Characterizing Mobile Advertising; In Proceedings of the 2012 ACM Internet Measurement Conference (IMC ’12); 2012; 14 pages.

Abstract

Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising.

The mobile advertisement (ad) ecosystem has been the target of prior research, but these works typically focused on a small set of apps or are from a user privacy perspective. In this work we make use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier with more than 3 million subscribers. We further take a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of energy on a mobile device.

Our analysis demonstrates a number of inefficiencies in today’s ad delivery. We discuss the benefits of well-known techniques, such as pre-fetching and caching, to limit the energy and net work signalling overhead caused by current systems. A prototype implementation on Android devices demonstrates an improvement of 50% in terms of energy consumption for offline ad-sponsored apps while limiting the amount of ad related traffic.

Mentions

Referenced

Mobile and Money | IAB, InMobi, Viggle

Via: Mobile and Money: Consumer Awareness and Adoption of Smartphone-based Financial Applications; IAB, InMobi & Viggle; 2013-04-11; 11 slides.

See backfill

Mentions