An Empirical Analysis of the Value of Information Sharing in the Market for Online Content | Beales, Eisenach

J. Howard Beales, Jeffrey A. Eisenach; An Empirical Analysis of the Value of Information Sharing in the Market for Online Content; Navigant Economics, commissioned by the Digital Advertising Alliance (DAA); 2014-01; 19 pages.


(from the Introduction)

This paper studies the importance of consumer-related information to the market for display advertising and, in turn, to the publishers who produce and distribute online content. Our study is based on two primary data sets. The first is a large, impression-level database of advertising placements during a one-week period in August 2013, provided by two anonymous companies that operate advertising exchanges with automated bidding. These data form the basis for an econometric analysis that allows us to measure the premium paid by advertisers for ads served to customers with cookies, and ultimately to assess the added economic value generated by information sharing in the online content market. The second data set, provided by Adomic, measures the relative prevalence of ads generated by different advertising models based on observations of display ad placements for the top 4000 publishers. This data set allows us to assess the relative significance of third party advertising technology models to the industry in general, and to smaller, “long-tail” web sites in particular.

The results of our econometric analysis corroborate and extend an emerging body of empirical work documenting the value of information sharing in online advertising. Our estimates indicate that advertisers place significantly greater value on users for whom more information is available, and our results are highly significant both in a statistical and economic sense: after controlling for other factors, the availability of cookies to capture user-specific information is found to increase the observed exchange transaction price by at least 60 percent relative to the average price (for users with “new” cookies), and by as much as 200 percent (for users with longer-lived cookies). In addition, the Adomic data reveal that even the largest publishers rely on third-party technology models to serve approximately half of their advertising impressions, while “long-tail” publishers rely upon third-party technology models for up to two thirds of their advertising volumes.



Source: Ad Week

Via: backfill

Fast Unfolding of Communities of Large Networks | Blondel, Guillaume, Lambiotte, Lefebre

Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre; Fast unfolding of communities in large networks; In Journal of Statistical Mechanics: Theory and Experiment; Volume 10; 2008; 12 pages; landing


We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks.


The Louvain Method


Via: backfill



Not Ready.

Architecture & Capability

  • <quote>The current version of the SmartThings Hub requires a persistent Internet connection in order to communicate…</quote>
  • The control channel for your home goes through their servers.
    • Which are located where?
    • How could that go wrong?
  • You need an account.
    • There are substantial conditions of use.
    • There may be fees now or then.
    • Accounts can be terminated (for any reason and for no reason).
  • Behaviors of your home are governed by their

Still Searching

Looking for something that behaves like the wiring in my house today.

  • I own it.
  • I operate it.
  • I control it.
  • No overlaid Terms of Service.  This is my house.
  • Information about it’s use doesn’t leave property that I own and control.
  • Control about it’s use doesn’t leave property that I own and control.

Economic Value of Online Advertising and Data

John Deighton, Peter A. Johnson; The Value of Data: Consequences for Insight, Innovation & Efficiency in the U.S. Economy; Direct Marketing Association (DMA); 2013-10-18; 103 pages; mentioned.



Ad-Supported Internet Responsible for 5.1 Million U.S. Jobs, Contributes $530 Billion to U.S. Economy in 2011 Alone, According to IAB Study; press release; 2012-10-01.
Teaser: New York, California, Washington, Massachusetts, and Illinois are the Top 5 States Where Companies Drive Digital Industry Jobs

Via: backfill, backfill | This Connection is (still) Untrusted

Nothing says “The Web is Misconfigured” quite like a security protocol failure notice: here.

Previously: 2013-10-01

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.


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.



  • 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.

DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps | Liu, Nath, Govindan, Liu

Bin Liu, Suman Nath, Ramesh Govindan, Jie Liu; DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps; In Proceedings of NSDI (NSDI); 2014; 15 pages.


Ad networks for mobile apps require inspection of the visual layout of their ads to detect certain types of placement frauds. Doing this manually is error prone, and does not scale to the sizes of today’s app stores. In this paper, we design a system called DECAF to automatically discover various placement frauds scalably and effectively. DECAF uses automated app navigation, together with optimizations to scan through a large number of visual elements within a limited time. It also includes a framework for efficiently detecting whether ads within an app violate an extensible set of rules that govern ad placement and display. We have implemented DECAF for Windows-based mobile platforms, and applied it to 1,150 tablet apps and 50,000 phone apps in order to characterize the prevalence of ad frauds. DECAF has been used by the ad fraud team in Microsoft and has helped find many instances of ad frauds.


Bin Liu, Suman Nath, Ramesh Govindan, Jie Liu; DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps; Technical Report 13-938; Viterbi School of Engineering; University of Southern California; 2013; 15 pages.

SCARF: a brain-based model for collaborating with and influencing others | David J. Rock

David J. Rock; SCARF: a brain-based model for collaborating with and influencing others; In NeuroLeadership JOURNAL; Issue 1; 2008; 10 pages.


David Rock
CEO, Results Coaching Systems International,
Faculty, CIMBA
Co-founder, NeuroLeadership Institute
Editor, NeuroLeadership Journal

<quote>David Rock is one of the thought leaders in the global coaching profession.</quote> about and bio



(the cited paper)

  • SCARF Model
    • Status
    • Certainty
    • Autonomy
    • Relatedness
    • Fairness
  • Responses
    • Approach
    • Avoid
  • Principle
    • stipulated, asserted, assumed
    • minimize danger, maximize reward
  • Medical/Scientific Foundational Model
    [true, fact-based, fabricated or fantastic; this is the machine model, as developed in the presentation]

    • the amygdala, “is almond-shaped”, can be “activated”, can be “overly vigilant”
    • the limbic system exists
    • the prefrontal cortex computes executive functions, they can increase or decrease, they require resources.
    • dopamine, has a level, causes or is caused by or is the definition of: emotional state.
    • cortisol, has a level, has a baseline level, lower is better, causes or is caused by or is the definition of: status.
    • circuitry (humans have), which can be “activated”; reward circuitry, fear circuitry, processing circuitry.
    • Orbital Frontal Cortex (OFC), computes an “error response” relative to the past.
    • <quote>The brain is a pattern-recognition machine that is constantly trying to predict the near future.</quote>; analogies of applications follow.
    • mental maps exist, can be recalled.
    • oxytocin, <quote>is a hormone produced naturally in the brain, and higher levels of this substance are associatedwith greater affiliative behavior.</quote>
    • insular, a brain region; can be “activated”, can be “involved in” or not; is “involved in” intense emotions such as disgust.
  • Enumerated Effects (quoting)
    1. When a human being senses a threat, resources available for overall executive functions in the prefrontal cortex decrease.
    2. When threatened, the increased overall activation in the brain inhibits people
      from perceiving the more subtle signals required for solving non-linear problems, involved in the insight or ‘aha!” experience.
    3. With the amygdala activated, the tendency is to generalize more, which increases the likelihood of accidental connections.There is a tendency to err on the safe side, shrinking from opportunities, as they are perceived to be more dangerous.
  • Undefined
    • non-linear problems
  • Status
    • pecking order
    • social rejection
    • social omission
    • reduce with self-assessment (which is not self-determination)
    • feedback systems
    • Intervention & Treatment
      • reward as increased status
  • Certainty
    • Process
      • uncertainty requires creates an “error response”
      • “error responses” must be handled with attention
      • attention removes from goals
    • Intervention & Treatment
      • reduce uncertainty
      • break down into small tasks
      • establish external non-negotiable rules, bounds, goals.
  • Autonomy
    • defined as: control, agency, choice (perceived choice)
    • correlation with: health (generalized “outcomes”)
    • Intervention & Treatment
      • increase it
      • bound it with policy & principles
      • ground-level point-of-need decisions
  • Relatedness
    • in-group vs out-group; tribes
    • safe social interactions
      • absence of which is defined as loneliness
    • social lubricants
      • alcohol
      • shaking hands
      • telling stories
    • correlated with, causes or is defined as: trust
    • Intervention & Treatment
      • reduce its lack (i.e. increase relatedness)
      • share personal stores
      • water cooler conversation time (citing Gallup 2008-11)
  • Fairness
    • Commences with money dividing & sharing studies-that-show of Tabibnia & Lieberman “at UCLA”; riffs against the study design.
    • Unfairness generates a threat response
    • Something about disgust (causal or correlated).
    • No (decreased) empathy for the unfair.
    • Intervention & Treatment
      • clear ground rules, expectations, objectives
      • self-determiation, self-direction & local decision-making
      • <quote>The issue of pay discrepancies in large organizations is a challenging one, and many employees are deeply unhappy to see another person working similar hours earning 100 times their salary. interestingly, it is the perception of fairness that is key, so even a slight reduction in senior executive salaries during a difficult time may go a long way to reducing a sense of unfairness.</quote>
  • Implications (for practice)
    • label
    • reappraise
    • avoid suppression (of the threat response)
    • <quote>Knowing the domains of SCARF also allows an individual to design ways to motivate themselves more effectively.</quote>
    • Possibilities for intervention (coaching) against pathologies within the framework
      • unclear expectations => uncertainty
      • micromanagement => autonomy
      • professional distance => relatedness
      • clear expectations & decisions => fairness


highlighted, cited, quoted within the text; in order of appearance

  • Evian Gordon, integrative neuroscientist
  • Naccache
  • Gordon
  • Lieberman & Eisenberger; The Pains and Pleasures of Social Life; NeuroLeadership JOURNAL; Issue 1; (same issue); 2008.
  • Friedman & Foster
  • Arnsten
  • Subramaniam
  • Phelps
  • Beaumeister, Bratslavsky, Vohs
  • Frederickson
  • Jung-Beeman
  • Michael Marmot; The Status Symdrome: How Social Standing Affects our Health and Longevity; Times Books; 2010-04-01, 2004; 340 pages; kindle: $10, paper: $4+SHT.
  • Sapolski
  • Chaio
  • Izuma
  • Eisenberger
  • Mitchell
  • Hedden, Garbrielli
  • Schultz
  • Scott, Dapretto
  • Hawkins
  • Mieka
  • Donny
  • Dworkin
  • Rodin
  • Carter, Pelphrey
  • Mitchell
  • Singer
  • John Cacioppo, a neuroscientist
  • Domes
  • Kosfield
  • Zak
  • Tabibnia, Lieberman
  • Singer
  • Semler
  • Lieberman
  • Ochsner & Gross
  • Goldin

Via: backfill

AdRob: Examining the Landscape and Impact of Android Application Plagiarism | Gibler, Stevens, Crussell, Chen, Zang, Choi

Clint Gibler, Ryan Stevens, Jonathan Crussell, Hao Chen, Hui Zang, Heesook Choi; AdRob: Examining the Landscape and Impact of Android Application Plagiarism; In Proceedings of MobiSys; 2013-06-23; 14 pages.


Malicious activities involving Android applications are rising rapidly. As prior work on cyber-crimes suggests, we need to understand the economic incentives of the criminals to design the most effective defenses. In this paper, we investigate application plagiarism on Android markets at a large scale. We take the first step to characterize plagiarized applications and estimate their impact on the original application developers. We first crawled 265,359 free applications from 17 Android markets around the world and ran a tool to identify similar applications (“clones”). Based on the data, we examined properties of the cloned applica tions, including their distribution across different markets, application categories, and ad libraries. Next, we examined how cloned applications affect the original developers. We captured HTTP advertising traffic generated by mobile ap plications at a tier-1 US cellular carrier for 12 days. To associate each Android application with its advertising traffic, we extracted a unique advertising identifier (called the client ID) from both the applications and the network traces. We estimate a lower bound on the advertising revenue that cloned applications siphon from the original developers, and the user base that cloned applications divert from the original applications. To the best of our knowledge, this is the first large scale study on the characteristics of cloned mobile applications and their impact on the original developers.


Clint Gibler; AdRob: Examining the Landscape and Impact of Android Application Plagiarism; On YouTube; 2013-04-11; 4:17.

How Facebook flipped the data centre hardware market | The Register

Jack Clark; How Facebook flipped the data centre hardware market; In The Register; 2014-02-20.
Teaser: The first rule of cloud fight club is…

Deep Links, continued


Recent & Definitive


  • Deep links as an SEO practice for “regular web sites” [not treated]
  • Deep links pointing into Appware/Adware on storebought applications (i.e. “mobile,” on a phone or tablet).

Roundup of the Genre

InContext 2014

InContext 2014 by EverythingMe.

On the notion of context and anticipation of needs in & around a device class that has no keyboard and lives with you.


video; 48:47; slides

Benedict Evans, Andreesen Horowitz
Q&A with Benedict Evans faciliated byTim Draper (he plays John Battelle in this vignette)

  • Recites the boring statistics,
    • up-and-to-the-right,
    • explosive growth,
    • gosh it’s really big,
    • <gee whizz!>
  • He compares
    • Yahoo 1996 to App Store 2014; replaced by Google (unstructured search)
    • Web vs Internet; the web is all “the internet does”
    • Mobile is pre-pagerank”
  • What happens in 5 years
    • He doesn’t know
    • Android (in 5 years)
    • Coding languages (in 5 years)
    • iBeacon
    • Access vs owning
  • Strategies
    • Apple: top down the stack (from control of the supply chain)
    • Google: up the stack (from hardware fragmentation)
  • Strategies
    • I know what I want => Google
    • I’m bored => Facebook, BuzzFeed, etc. etc.
    • Demand Generation => empty
  • Smart(phones)
    • Are inherently social
    • Take away “winner take all”
  • Cards as content packages
    • Can be shared
    • Can be syndicated
    • Contradiction:
      • Atomised Content
      • App Silos
  • What’s Already Known
    • Contacts
    • Calendar
    • Apps frequently used
    • Travel patterns
    • etc.
  • Context
    • Google Now
    • or other similar things
  • But
    • The Filter Bubble
    • The Uncanny Valley
  • Something about ‘Ecosystem Cohorts’
  • Neither Apple nor Google “will win”; ther is no “winner take all” dynamic.


  • Some generalized whining
    • that intent and preference prediction won’t work;
      story about Pandora from Tim Draper.
    • that Google Now is ‘closed’ to (his) startups.
  • Unclear that a human butler (ahem, “life coach”) could live achive these standards.
  • Something about the music industry
    • It’s a distribution business
    • A quote from Mic Jagger about musicians not being paid 1970s-1995, not before, not after.
  • Draper on tablet vs PC
    • Tablet is for reading (&deleting)
    • PC is for creating

Bytes of Context

video; 25:42

  • Andreas Gal, Mozilla,
  • Andy Grignon, Quake Labs/Eightly, moderator
  • Andy Hickl, A.R.O,
  • G D Ramkumar, Swell.
  • Dave Smiddy, Alohar.

Global Context

video; 28:27

  • Josh Constine, TechCrunch, moderator
  • Brendan Eich from Mozilla,
  • Seth Sternberg from Google,
  • Ami Ben David from EverythingMe.

Mozilla Product Announcement

video; 29:52

  • Ami Ben David, Co-founder and Head of Strategy and Marketing at EverythingMe,
  • Andreas Gal, VP Mobile at Mozilla.

Firefox Launcher for Android by Mozilla

Wearables in Context

video; 33:08

  • Peter Berger, People+,
  • Christina Farr, VentureBeat,
  • Monisha Perkash, LUMO,
  • Rackspace’s Robert Scoble, moderator
  • Redg Snodgrass, Wearable World.

Via: backfill

Xbox One busted at only sixty days out with a grinding noise from the DVD/BlueRay

Xbox One busted at only sixty days out with a grinding noise from the DVD/BlueRay.  Won’t play anything.


  • Maybe you can (still) get a replacement unit from M$.
  • Percussive maintenance; as shown here



Big Privacy: Bridging Big Data and the Personal Data Ecosystem through Privacy by Design | Cavoukian, Reed

Ann Cavoukian, Drummond Reed; Big Privacy: Bridging Big Data and the Personal Data Ecosystem through Privacy by Design; 2013-12; 37 pages.


Via: backfill



  • Big Privacy, contra Big Data.
  • Cloud Service Provider (CSP), contra Internet Service Provider (ISP)

Elements of Big Privacy

  1. Personal Clouds
  2. Semantic Data Interchange
  3. Trust Frameworks
  4. Identity and Data Portability
  5. Data-By-Reference (or Subscription)
  6. Accountable Pseudonyms
  7. Contractual Data Anonymization

Principles of Privacy by Design

  1. Proactive not Reactive; Preventative not Remedial
  2. Privacy as the Default Setting
  3. Privacy Embedded into Design
  4. Full Functionality – Positive-Sum, not Zero-Sum
  5. End-to-End Security – Full Lifecycle Protection
  6. Visibility and Transparency – Keep it Open
  7. Respect for User Privacy – Keep it User-Centric

Big Data Life Cycle

  • Data Harvesting
  • Data Mining
    • correlations
  • Application
    • discovery (to find)
    • prediction
    • value (predict value)
    • recommend


  • Foster Provost, Tom Fawcett; Data Science for Business: What you need to know about data mining and data-analytic thinking; O’Reilly Media; 2013-07-27; 414 pages; kindle: $10, paper: $22.
  • information
    • control
    • specificity
    • self-determination
    • consent
    • secrecy
    • purpose specificity
    • limitation on use
  • personal cloud
    • Gartner said, in 2012, so it must be true.
  • Personal Data
    • Suffix
      • Store
      • Locker
      • Vault
    • (Closed) Product Lines
      • Dropbox,
      • Google Drive,
      • Apple’s iCloud.
    • (Open) Source
      • OwnCloud,
      • remoteStorage,
      • Cloud OS,
      • XDI2.
  • Smart Data=> is Digital Rights Management (DRM)
    • Magic Pixie Dust
    • Data that “thinks for itself”
    • cloak of intelligence
    • <quote>The goal of the XDI Technical Committee is not just a semantic data format, but a semantic data protocol that enables machines to literally “talk” to each other in a common language</quote>
    • <quote>it can use semantic statements to describe the rights
      and permissions that apply to a specific set of data in a specific context.</quote>
  • Zooko Wilcox-O’Hearn; Zooko’s Triangle
    A folk theorem:  (digital) identifiers at a distance can be any of Memorable, Secure, Global; but not all (pick at most two).
  • The term “informational self-determination” was first used in a German constitutional ruling concerning personal information collected during Germany’s 1983 census.
  • Respect Network

Table of Contents

  1. Introduction
  2. Big Data, Privacy Challenges, and the Need to Restore Trust
  3. A Definition of Big Privacy
  4. The Seven Architectural Elements of Big Privacy
  5. Exemplar: Respect Network™ and the OASIS XDI Protocol
  6. How Big Privacy Applies the 7 Foundational Principles of Privacy by Design
  7. Conclusion


Narrative Clip, still not ready




  • Stated in Requirements To Use The Narrative Clip
  • <quote>Not supported yet: Linux, Blackberry, Windows phone, etc.</quote>
  • <quote>The Narrative Clip can’t be used as a regular USB memory stick because it doesn’t mount as USB mass-storage device.</quote> [and not as MTP either?]


  • Your photos are only stored in their cloud “for easy access.”
  • You have to buy a “Narrative Cloud subscription” to access them.
  • Technical Specifications

Reasonable & Prudent Thinking

  • Photos are timestamped and (GPS) geotagged.
  • No guarantee of continued access after the company folds.
  • Seems ripe for builtin backdoors [for improper elements].
  • They’ll build a map of your facilities based on your photos.

And … although Privacy Policies are silly because they are just that “policies” and they can change at any time for any reason, and for no reason … they don’t even seem to have hired up someone to write them a boilerplate.

When would it be ready?

  • Upload to my own gear, that I own, gear that is stored in my own house.
  • No ongoing operating fees.
  • No “cloud” access.
  • Standard USB access to the data on the device
    • MTP
    • Mass Storage
  • i.e. Supports Linux.

Anyone considered this?



Via: backfill