De-Anonymizing Web Browsing Data with Social Networks | Su, Shukla, Goel, Narayanan

Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan; De-Anonymizing Web Browsing Data with Social Networks; draft; In Some Venue Surely (they will publish this somewhere, it is so very nicely formatted); 2017-05; 9 pages.

Abstract

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest-scale demonstrated de-anonymization to date.

Promotions

  • Ad Networks Can Personally Identify Web Users; Wendy Davis; In MediaPost; 2017-01-20.
    <quote> The authors tested their theory by recruiting 400 people who allowed their Web browsing histories to be tracked, and then comparing the sites they visited to sites mentioned in Twitter accounts they followed. The researchers say they were able to use that method to identify more than 70% of the volunteers.</quote>

Marketers Find You at 2:00 a.m. | WSJ

Marketers Find You at 2:00 a.m.; Charlie Wells; In The Wall Street Journal (WSJ); 2016-04-12.
Teaser: You know you want a breakfast burrito; companies use social media for ads in the wee hours

tl;dr → day parting works, sortof, maybe.

Mentions

  • Folgers Coffee
    • “Wakin’ Up Alarm Clock”
    • first release, 2013.
  • Influence Central,
    various factoids

Exemplars

Publishers
  • Facebook
  • Instagram
  • Twitter
  • YouTube
Consumers
  • Shaaz Nasir, age 27, Ottawa, Canada,
Advertisers
  • Asos, a clothing retailer, UK
  • Folgers Coffee
  • Gilette, Proctor & Gamble
  • Taco Bell

Quoted

  • Laura Beaudin, partner, customer strategy and marketing, Bain.
  • Scott Heimes, chief marketing officer, SendGrid.
  • Kevin Akeroyd, general manager and senior vice president, Oracle Marketing Cloud, Oracle Inc.
  • Klodiana Lanaj, assistant professor, Warrington College of Business, University of Florida.

Smartphone Sensing | Emiliano Miluzzo

Emiliano Miluzzo; Smartphone Sensing; PhD. Dissertation; Dartmouth College; 2011-05; 147 pages.

Abstract

The increasing popularity of smartphones with their embedded sensing capability and the availability of new application distribution channels, such as, the Apple AppStore and the Google Android Market, is giving researchers a unique opportunity to deploy mobile sensing applications at unprecedented scale and collect sensor data way beyond the boundaries of traditional small-scale research laboratory deployments. This thesis makes a number of contributions to smartphone sensing by introducing new sensing models, algorithms, applications, and systems.

First, we propose CenceMe, the first large-scale personal and social sensing application for smartphones, which allows users to share their real-time “sensing presence” (i.e., activity and context) with friends using the phone, web, and social network sites (i.e., Facebook, Myspace, Twitter). CenceMe exploits the smartphone’s onboard sensors (viz. accelerometer, microphone, GPS, Bluetooth, WiFi, camera) and lightweight, efficient machine learning algorithms on the phone and backend servers to automatically infer people’s activity and social context (e.g., having a conversation, in a meeting, at a party). The development, deployment, and evaluation of CenceMe opened up new problems also studied in this dissertation.

Sensing with smartphones presents several technical challenges that need to be surmounted; for example, the smartphone’s sensing context (i.e., the position of the phone relative to the event being sensed varies over time) and limited computational resources present important challenges that limit the inference accuracy using phones. To address these challenges, we propose an “evolve-pool-collaborate” model that allows smartphones to automatically adapt to new environments and conduct collaborative sensing among co-located phones resulting in increased robustness and classification accuracy of smartphone sensing in the wild. We call this system, Darwin Phones.

The final contribution of this dissertation explores a new mobile sensing application called VibN, which continuously runs on smartphones allowing users to view live feeds associated with hotspots in a city; that is, what is going on at different locations, the number of people and demographics, and the context of a particular place. VibN addresses a number of critical problems to the success of smartphone sensing, such as, running continuous sensing algorithms on resource limited smartphones, resolving privacy issues, and developing a sensor data validation methodology for applications released via the app stores (i.e., validating sensor data and identifying patterns without any notion of ground truth evidence). Such a methodology is crucial to the large-scale adoption of smartphone sensing in the future.

Smartphone sensing is an emerging field that requires significant advances in mobile computing, machine learning, and systems design. It is an exciting area of research that is cross-disciplinary and likely to touch on many application areas and scientific domains moving forward. The work presented in this dissertation identifies new problems and solutions that help advance our understanding in what is now a fast-moving area of research.

Snapchat Challenges Twitter And Facebook For Sports Talk | TechCrunch

Snapchat Challenges Twitter And Facebook For Sports Talk; In TechCrunch; 2016-01-29.
Teaser: Snapchat Challenges Twitter And Facebook For Sports Talk

tl;dr → Shapchat adds geofilters to control the conversation (overlay results, yet avoid copyright claims).

Mentions

  • Snapchat
    • Live Story (a feature, a product trade name)
  • National Football League (NFL)
  • <quote>Live Score geofilters saw 20 million views from 51 games.</quote>
  • Twitter
    • Moments (a feature, a product trade name)
    • Hashtags (a feature, a product trade name, you know the #-character)
  • Facebook
    • News Feed
    • Wall (Feed)
    • Instagram
  • Concept
    feels like this is barely rephrased from Snapchat press relations material
    <quote>On Facebook you might post once a day. On Snapchat or Twitter, a few times. But on Snapchat, you can go HAM. Share your pre-game, entrance to the stadium, outfit, seats, the action, the half-time, game-winning play, the celebration and everything after. All Snapchat has to do is convince you what you’re doing is special, and that’s what the Live Score filters do. The real-time sports social media race just heated up.</quote>

The Death of Blogging | Charles Miller

Charles Miller; The Death of Blogging; In His Blog entitled The Fishbowl; 2015-07-08.

tl;dr → blogs are for corporations & linkbaitists (self-styled “journos”) nowadays.

Outline

  • Digg stole its community
  • Twitter stole its small-talk
  • Tumblr stole its future
  • Centralization and lock-in won.

 Mentions

  • <quote>In the end, the distributed, do-it-yourself web was just too hard.</quote>
  • <quote>In every case, a closed, proprietary system took some ingredient of the self-publishing crack bloggers discovered in the early 2000s and distilled it into a product that was easier to use, and that people were willing to adopt even though it meant losing the freedom of openness, interoperability and owning your own words.</quote>

The rise of “homeless” media | Francesco Marconi, Medium

Francesco Marconi; The rise of “homeless” media; In Medium; 2013-11-24.
Teaser: We could soon see the emergence of a new wave of publishers that don’t require home pages or apps; their sole purpose is to syndicate content through different channels and social platforms.

Francesco Marconi is

  • Strategy Manager, The Associated Press
  • Fellow, Tow Center for Digital Journalism, Columbia Journalism School.
  • [Honorific], Berkman Center, Harvard University.

tl;dr → self-styled media theorist for a news syndicator self-consciously writes about media syndication business model, publishing his work on a media platform, Medium.

Mentions

  • “homeless” media
  • “viral” content.
  • advice is given to all stakeholders
    • media generators, aggregators
    • platform operators
    • “capital”
  • Denouement
    <quote>With native content consumption on third-party platforms growing, will it still be relevant for media companies to invest significant resources on running and maintaining their websites and mobile apps?</quote>
    Betteridge’s Law → Answer: no.

Listicles

Why It Will Happen

  1. Mobile and app consumption is increasing
    cites

  2. Third parties are creating platforms for publishers to easily distribute and monetize content.
    cites:

    • The Washington Post on Facebook Instant Articles
    • CNN on Snapchat Discover
    • Google Accelerated Mobile Pages (AMP)
    • Twitter Moments.

What To Do

  • Drive engagement, whatever that means
  • Develop unique programming
  • Acquire distinctive voices
  • Be bizdev-friendly with The Platforms, they run things now
  • Keep expenses down
    remember, the cultural output, the work product is just 500 words + a picture.

Platforms

Exemplars

Via Search Engine Optimization (SEO)

  • Drudge Report
  • The Huffington Post

via Social Media (The Linkbait)

  • BuzzFeed
  • CNN
  • Vice
  • Vox
  • The Washington Post

Referenced

Google, Facebook, Yahoo And Verizon (And Comcast, AT&T And Twitter) Have Chosen Their New Battleground | TechCrunch

Google, Facebook, Yahoo And Verizon (And Comcast, AT&T And Twitter) Have Chosen Their New Battleground; ; In TechCrunch; 2015-10-30.
Jeff Segal is the director of strategy consulting at Magid Associates.

tl;dr →”battleground” is a metaphor for competition; a Grand Strategy is announced; Display Advertising becomes Over-The-Top (OTT) against (linear) Big Television.

Mentions

  • Over The Top (OTT)
  • Distributors
    • AOL
    • AT&T
    • Comcast
    • Facebook
    • Google
    • Twitter
    • Verizon
    • Yahoo
  • Producers
    • Portals
      • Facebook
      • YouTube
    • Sports
      • CNN
      • ESPN
      • Fox Sports
  • Claims
    • Display Advertising
      • 35 display ad sellers account for roughly 85 percent of total digital advertising revenues.
    • Video Advertising
      • very few pure-play video publishers.
      • the majority of time spent is the mid-to-long tail.
      • the mid-to-long tail has almost no video.
      • (online) video generates a 6-8X premium over display advertising.
  • YouTube
    • a video syndication product extension
    • is “AdSense for Video”
    • DoubleClick for Publishers (DFP)
    • something about <quote>programmatically creating new video inventory for publishers that don’t necessarily create any video content</quote>
    • YouTube is User-Generated Content (UGC)
      i.e. trash supply, not brand-safe.
  • Business Structure
    three-legged stool, a metaphor

    • a content network
    • ad platform
    • first-party data (for targeting).
  • Facebook
    • Distribution
      • LiveRail, a video SSP
      • The New York Times (via Facebook Instant Articles)
    • Ad System: sure.
    • Party Date: yes
  • Verizon
    • AOL On Network
      • 4,000 sites
    • Adap.TV
    • Party Data: Something about <quote>extensive first-party data</quote>.
  • Yahoo
    • BrightRoll, an ad platform
    • Party Data: yes.
    • missing: a publisher network
  • Unclear
    • AT&T
    • Comcast
    • Twitter
      • Twitter Amplify (is what, exactly?)

Notes from the Platform’s Edge | The Awl

Notes from the Platform’s Edge; ; Series the content wars, in The Awl; 2015-10-13.
Teaser: Platforms for everyone, publications for no one

tl;dr → discursive, statements, yet thirty questions.  Platforms (Facebook, Twitter, et al.) are reducing sharing to linkbaitists; clickthroughs are shrinking.  The platforms aren’t sure how to behave, but they are the internet now, they are the gatekeeperss. Only time will tell.

 

Is Silicon Valley in Another Bubble … and What Could Burst It? | Vanity Fair

Is Silicon Valley in Another Bubble … and What Could Burst It?; In Vanity Fair; 2015-09-01.
Teaser: With the tech industry awash in cash and 100 “unicorn” start-ups now valued at $1 billion or more, Silicon Valley can’t escape the question. Nick Bilton [opines]

tl;dr → yes. separately noted.