Literature Review

Web privacy measurement is a nascent field, with significant contributions developed by academic computer scientists and others interested in discovering tracking vectors and quantifying them. At Web Privacy Measurement 2012, leaders in the field attempted to formalize these efforts.

The Electronic Privacy Information Center made the earliest attempts to enumerate privacy practices in a systematic fashion. In June 1997, it released Surfer Beware: Personal Privacy and the Internet, a survey of the top 100 websites. Only 17 of the top 100 websites had privacy policies. Twenty-three sites used cookies, although it appears that EPIC used a “surface crawl” to detect those cookies, meaning that it only visited the homepage of the site and did not click other links. By 2009, Soltani et al. found cookies on 98 of the top 100 sites, and by 2011, Ayenson et al. found cookies on all 100 most popular sites (see discussion below).

In Surfer Beware II: Notice is Not Enough, published in June 1998, EPIC surveyed websites of companies that had recently joined the Direct Marketing Association. At the time, the Direct Marketing
Association (DMA) had committed to basic privacy protections, including notice and an ability for consumers to opt out. EPIC found that there were 76 new members of the DMA, but only 40 had websites. Of those 40, all collected personal information. Only eight of the sites had a privacy policy

The Federal Trade Commission conducted the first large-scale privacy measurement study in Privacy Online: A Report to Congress. Released in June 1998, the Commission studied the privacy practices of 1,402 websites, using a sophisticated sample procedure to ensure that a variety of consumer-oriented websites were studied (health, retail, financial, sites directed to children, and the most popular websites). The FTC found that, “the vast majority of Web sites — upward of 85% — collect personal information from consumers. Few of the sites — only 14% in the Commission’s random sample of commercial Web sites — provide any notice with respect to their information practices, and fewer still — approximately 2% — provide notice by means of a comprehensive privacy policy.”

In EPIC’s Surfer Beware III: Privacy Policies without Privacy Protection, the group surveyed the practices of 100 ecommerce sites. This was the most comprehensive, but last of the EPIC surveys. It evaluated sites for compliance with a full range of fair information practices, such as whether the site collected personal information, whether the site linked to a privacy policy, whether the site had agreed to a seal program, and whether users had access and correction rights for personal information. Eighty-six of the sites used cookies, 18 lacked privacy policies, and 35 had some form of network advertiser active on the site. The text of the report makes it clear that EPIC evaluated both the privacy politics of these sites and tested them to see whether they were setting cookies. However, it is unclear whether EPIC performed a surface crawl of just the homepage or a deeper crawl that explored more of the site.

In May 2000, the Federal Trade Commission released a survey of sites that detected third party cookies. In its study, the FTC drew from two groups of websites: those with over 39,000 visits a month and a second sample of popular sites (91 of the top 100). The FTC found that, “57% of the sites in the Random Sample and 78% of the sites in the Most Popular Group allow the placement of cookies by
third parties…. The majority of the third- party cookies in the Random Sample and in the Most Popular Group are from network advertising companies that engage in online profiling.”

In a multiple-year study of 1,200 websites, Bala Krishnamurthy and Craig Wills found increasing
collection of information about users from an increasingly concentrated group of tracking companies. Krishnamurthy and Wills describe what we call “DNS aliasing” in their paper (this was also described in their 2006 paper), a practice where, “…what appeared to be a server in one organization (e.g. was actually a DNS CNAME alias to a server ( in another organization (Omniture).” They found a massive increase in such aliasing: “…the percentage of first-party servers with multiple top third-party domains has risen from 24% in Oct’05 to 52% in Sep’08…This increase is significant because it shows that now for a majority of these first-party servers, users are being tracked by two and more third-party entities.” It is also significant because through DNS aliasing, tracking companies can present cookies to users directly as first parties, thereby circumventing third party cookie blocking.

Through decoding aliased domains, Krishnamurthy and Wills found that third party trackers were becoming more concentrated. Sampling from five periods, concentration grew from 40% in October 2005 to 70% in September 2008. Further, they found that, “The overall share of the top-five families: Google, Omniture, Microsoft, Yahoo and AOL  extends to more than 75% of our core test set with Google alone having a penetration of nearly 60%.”

In June 2009, Gomez et al. published the KnowPrivacy report. The report focused on several areas of
consumer privacy, and featured a large-scale crawl of sites based upon data from Ghostery. Google-owned trackers were present on over 88% of a sample of 393,829 distinct domains. Further, in a survey of the top 100 sites, Google Analytics appeared on 81 of them.

In August 2009, Soltani et al. demonstrated that popular websites were using “Flash cookies” to track
users. Some advertisers had adopted this technology because it allowed persistent tracking even where users had taken steps to avoid web profiling. Soltani et al. also demonstrated “respawning” on top sites with Flash technology. This allowed sites to reinstate HTTP cookies deleted by a user, making tracking more resistant to users’ privacy-seeking behaviors. In a survey of the top 100 sites according to Quantcast, Soltani et al. found 3602 cookies set on 98 of the top 100 sites. They also found 281 Flash Cookies set on 54 of the top 100 sites.

In July 2010, Julia Angwin, Tom McGinty, and Ashkan Soltani of the Wall Street Journal reported that in a scan of the top 50 sites, 3,180 “tracking files” (this comprised HTTP cookies, Flash cookies, and web beacons) were detected. Twelve sites set over 100 each.

In 2010, Michael Coates surveyed the top 1,000 websites in order to determine how many were using HTTPS. Coates sent a basic HTTPS request to these sites, and they responded with 559 cookies. Coates’ method appeared to not collect any third party cookies.

Flash cookies have become a major focus of research. In 2001, McDonald and Cranor of Carnegie Mellon investigated the presence of Flash cookies on websites. They found a dramatic decline from the Soltani et al. investigation in 2009. McDonald and Cranor found Flash cookies on only 20 of the top 100 sites. They were also careful to attempt to determine whether Flash cookie values were unique or not—six of the top 100 sites had Flash cookies that were not unique, and thus probably not used to track individuals.

Krishnamurthy et al. have made significant contributions to the study of privacy “leakage.” In a study of
websites that required registration, they found that a majority of the popular sites they analyzed “directly leak sensitive and identifiable information to third-party aggregators.” The problem they identified was widespread: “56% of the 120 popular sites in our study (75% if we include userids) directly leak sensitive and identifiable in formation to third-party aggregators.”

In July 2011, Stanford Law/Computer Science graduate student Jonathan Mayer released “FourthParty,” an “open-source platform for measuring dynamic web content.” Mayer has posted the raw data from web crawls made with the platform, and has released two reports flowing from the system. In the first, Mayer tested how members of the Network Advertising Initiative (NAI) interpret opt outs. The NAI considers the scope of opt out rights to pertain only to targeting ads, not to tracking. Thus, if a consumer opts out, NAI members may still track them. Mayer found that half of the NAI members tested (N=64) still used tracking cookies after an opt out was expressed.

In the second, Mayer found that in developing FourthParty, he detected “browser history stealing.” This is a practice where a website, “exploits link styling to learn a user’s web browsing history. The approach is simple: to test whether the user has visited a link, add it to a page and check how it’s styled.”

In August 2011, Ayenson et al. surveyed the top 100 web sites, simulating a user session by clicking on 10 random links on each site. Cookies were detected on all top 100 sites. The group found 5,675 cookies, 4,615 of which were set by third parties. Six-hundred third-party hosts were detected. Google-controlled cookies were present on 97 of the top 100 sites, including popular government websites.

Ayenson et al. found that 17 sites were using HTML5 local storage, and seven of those sites had HTML5 local storage and HTTP cookies with matching values. Flash cookies were present on 37 of the top 100 sites.

In October 2011, Jonathan Mayer tested signup and interaction on 185 of the Quantcast top 250 sites.  He found 113 of the sample leaked userids or usernames to third parties.

In the Pixel Perfect: Fingerprinting Canvas in HTML5, a study done in 2012 by Mowery and Shacham, the relationship between the web browser and the operating system is investigated in order to understand how each system creates its own fingerprint. Three-dimensional graphics (WebGL) and browser font are used to produce an unique image which is used as a fingerprint.

Understanding What They Do With What They Know, released in 2012 by Wills, investigated what Web advertisers do with the information gathered from a person.  Advertisements shown to users during experimental controlled browsing sessions and personal interests shown in Ad Preference Managers were analyzed and discussed.  The authors found unforeseen results for the Google ad network they studied. Advertisements generated in real time, related to a person’s private life appeared in the data collected. The study also found that a user’s browsing behavior on sites other than Facebook, while logged in, did not correlate with the ads shown to the user on Facebook.

FPDetective: dusting the web for fingerprinters, released in 2013 by Acar, discusses how FPDetective framework detects and analyzes web-based fingerprints.The study also found weaknesses in both the Tor browser and Firegloves, two systems that pride themselves on concealing fingerprints, that would allow online trackers to determine user’s fingerprint.   FPDetective was used as a crawler to visit websites, pick up on properties that relate to a user’s fingerprint and store the information in a database.

Malandrino, Krishnamurthy et al.’s Privacy Awareness about Information Leakage: Who Knows About Me? study is concerned with users’ lack of access to and awareness of their private information online.  The study compared the amount of sensitive information leaked when using different privacy protection tools, including NoTrace, AdBlock Plus, Ghostery, NoScript and RequestPolicy.  Although they concluded that no privacy extension can fully protect users online, NoTrace was praised for showing users a behind-the-scenes view of the availability of their personal information to trackers.

Olejnik et al. in Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns, investigated how history-based user fingerprinting is done.  With a dataset of 300k users’ web browsing histories, the pages users visited and sites they repeatedly returned to, the study found that more than 69% of users have a unique fingerprint.  Consequently, web browsing histories can easily be traced to particular users and their personal preferences by web authors.

Mayer and Mitchell explored third-party tracking and advertising in their study, Third-Party Web Tracking: Policy and Technology. They used FourthParty, an open-source web platform that measures dynamic web content, to crawl Alexa’s Top 500 sites. In the study, Mayer and Mitchell found that out of the 11 ad-blocking tools they tested, all of them blocked third party advertising.  However, the ad-blocking tools didn’t differentiate between advertising content and advertising-related tracking content.  They concluded that without the configuration of options in ad blocking software, it can only be slightly effective, so is primarily a solution for more advanced users.  

In Privacy and Online Social Networks: Can Colorless Green Ideas Sleep Furiously, Krishnamurthy led a discussion on online social networks and their responsibility as the party with the most detail about its users’ interactions, to be more transparent about the flow of users’ private information to other sites over time. Krishnamurthy believed that with more transparency and tools like the Facebook extension, Privacy IQ, users can get a better understanding of their privacy and what actions they may need to take to attain their preferred level of privacy on social networks. He suggested that OSNs have the means to bridge the gap between users and privacy protection and should be invested in doing so.

In Fast and Reliable Browser Identification with JavaScript Engine Fingerprinting, Mulazzani et al. also studied how spoofing a user agent string doesn’t successfully hide the user’s identity. They tested the underlying Javascript engine in multiple browsers and browser versions to find that they could reliably determine the user’s browser without regard to the user agent at all.

In the 2013 study, Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting, at University of California, Santa Barbara, on web-based device fingerprinting, Nikiforakis et al. surveyed over 800,000 users and conducted a 20-page crawl of Alexa’s top 10,000 websites. They found that users who install browser or user agent spoofing extensions create a more unique fingerprint for themselves. The study found that the extensions aren’t able to completely hide the browser’s identity (i.e., unable to spoof particular methods or properties), resulting in the user being even more recognizable.

In a 2014 device fingerprinting study, Obfuscation For and Against Device Fingerprinting, Acar discusses the concepts of power and knowledge asymmetry, in relation to device fingerprinting, as the user has no knowledge of where their data is used and no control over how it is gathered. Acar also comments on the uselessness of spoofing user agents since it is not always a reliable way to prevent tracking. It is concluded that more effective tools like obfuscation with the Tor browser are needed to combat fingerprinting.

In Cookies that give you away: Evaluating the surveillance implications of web tracking, released in 2014 by Reisman et al., it was discovered that multiple web pages with embedded trackers can connect a user’s web page visits back to the specific user. By using simulated browsing profiles, it was also discovered that over half of the most popular web pages that have embedded trackers leak a user’s identity to other parties.

In The Web Never Forgets: Persistent Tracking Mechanisms in the Wild, a study done in 2014 by Acar et al., focused on a tracking mechanism called canvas fingerprinting. A canvas fingerprint is an image with text that is drawn in the browser and sent to the requesting site the user is on.  This type of tracking produces a unique fingerprint without the user being aware, because each system produces a different image. Cookie syncing and respawning are also tracking techniques discussed in this paper to be wary of because they allow domain-to-domain communication and consistent tracking after a user wipes their cookies, respectively.  

Reverse Timeline



Major Finding

Sample Size

Acar et al.


A new technique called canvas fingerprinting is used to track users– 5.5% of the sample size ran canvas fingerprinting on their homepage

Top 100,000 sites from Alexa

Reisman et al.


Embedded trackers in website allow users to be tracked

Top 500 Alexa websites



It discussed that 145 of the

top ten thousand websites use Flash-based fingerprinting  and 400 of the top one million websites use JavaScript-based fingerprinting.

Top ten thousand   million websites from the Alexa database

Mulazzani et al.


JavaScript engine fingerprinting

is a practical approach to identify and verify an specific browser,

even for mobile technologies.

189 tests.



The FPDetective framework found that 404 sites in the sample size gather users’ fingerprints through their homepages using Javascript-based font probing

Top million websites from Alexa



Current information protection methods of online social networks (OSNs) are not adequate enough to prevent users’ data from being shared by parties across sites


Nikiforakis et al.


40 sites (0.4% of

the Alexa top 10,000) are utilizing fingerprinting code from

the three commercial providers mentioned in this work

20 page crawl of each of the Alexa top 10,000 sites

Malandrino, Krishnamurthy et al.


Aggregators are able to collect much information about users’ online profiles; one of the top ten aggregators in this study is able to collect 87% of a user’s private data

Top 100 sites from 15 Alexa categories

Olejnik et al.


More than 69% of users tested have a unique fingerprint, some larger than 18 bits, just based on their browsing histories

368, 284 users’ web histories



Advertisements are generated based on a person’s intimate and private life such their financial life and sexual orientation.

15-20 sessions to visit other sites, while logged into Facebook

Mowery, Shacham


Revolutionary system to produce fingerprints based on browser font and WebGL rendering

Samples from 300 distinct members of the

Mechanical Turk Marketplace (AI service from Amazon)

Mayer, Mitchell


Ad blocking software is not effective for less advanced users. Out of the 11 ad-blocking tools tested, all blocked third-party advertising but allowed tracking.

3 consecutive crawls of the Alexa top 500 sites



Most popular websites were “leaking” usernames and userids to third parties.

185 of the Quantcast top 250

Ayenson et al.


5675 HTTP cookies detected, 4615 of which were third party.  37 sites with 100 Flash cookies detected.  All top websites had cookies.

Top 100 sites, 10-click user session simulated



Network Advertising Initiative members continued to use tracking cookies after opt out

64 of the Network Advertising Initiative Members

Krishnamurthy & Wills


Majority of popular websites with registration leaking personal data to third parties

Array of popular websites that required registration

McDonald & Cranor


Flash cookies present on 20 of top 100 sites

Surface crawl of homepages of top 100 sites



559 first party cookies detected

Limited HTTPS request to top 1,000 sites

Angwin et al. (Wall Street Journal What They Know)


3,180 tracking mechanisms detected.  Only one site lacked cookies.

Top 50 sites, 20-click user session simulated

Gomez et al. (KnowPrivacy Report)


Google-owned web beacons were present on 88% of a large sample of websites

393,829 unique domains

Soltani et al.


3602 HTTP cookies detected, 281 Flash cookies detected.  98 of the top 100 sites had cookies.

Top 100 sites, 10-click user session simulated

Krishnamurthy et al.


Large increase in DNS aliasing; penetration of major third party trackers to 75% of sample sites

1,200 sites scanned over four years



57% of the sites in the Random Sample and 78% of the sites in the Most Popular Group set cookies.

Random sample of 335 sites and 91 of top 100 sites

EPIC Surfer Beware III


86 used cookies.

100 ecommerce sites

FTC Privacy Online


Most websites collect personal info, but only 14% have privacy notices


EPIC Surfer Beware II


Few of the newest DMA members had privacy policies

New DMA members

EPIC Surfer Beware I


Homepages of 23 sites used cookies

Top 100



Acar, Gunes, Eubank, Christian, Englehardt, Steven, Juarez, Marc, Narayanan, Arvind, Diaz, Claudia, The Web Never Forgets: Persistent Tracking Mechanisms in the Wild, July 1, 2014, available at

Acar, Gunes, Obfuscation For and Against Device Fingerprinting Position Paper for Symposium on Obfuscation, Feb. 15, 2014, available at

Acar, Gunes, FPDetective: Dusting the Web for Fingerprinters, 2013, available at

Julia Angwin, The Web’s New Gold Mine: Your Secrets, A Journal investigation finds that one of the fastest-growing businesses on the Internet is the business of spying on consumers, Wall Street Journal, Jul. 30, 2010, available at

Ayenson, Mika, Wambach, Dietrich James, Soltani, Ashkan, Good, Nathan and Hoofnagle, Chris Jay, Flash Cookies and Privacy II: Now with HTML5 and ETag Respawning (July 29, 2011)available at:

Michael Coates, A Study of HTTPOnly and Secure Cookie Flags for the Top 1000 Websites, Dec. 28, 2010, available at

Electronic Privacy Information Center, Surfer Beware: Personal Privacy and the Internet, Jun. 1997, available at

Electronic Privacy Information Center, Surfer Beware II: Notice is Not Enough, Jun. 1998, available at

Electronic Privacy Information Center, Surfer Beware III: Privacy Policies without Privacy Protection, Dec. 1999, available at

Federal Trade Commission, Privacy Online: A Report to Congress, Jun. 1998, available at

Federal Trade Commission, Privacy Online: Fair Information Practices In the Electronic Marketplace: A Report to Congress, May 2000, available at

Joshua Gomez, Travis Pinnick, and Ashkan Soltani, KnowPrivacy (Jun. 1, 2009), available at

Krishnamurthy, B., & Wills, C., Privacy diffusion on the web: A longitudinal perspective, Proceedings of the 18th ACM international conference on World wide web (2009)(p. 541-550), available at

Krishnamurthy, B., Naryshkin, K., & Wills, C. E., Privacy leakage vs. Protection measures: the growing disconnect, presented at W2SP 2011: WEB 2.0 SECURITY AND PRIVACY 2011 (2011), available at

Krishnamurthy, B., Privacy and Online Social Networks: Can Colorless Green Ideas Sleep Furiously?, 2013, available at

Malandrino, Delfina, Petta, Andrea, Scarano, Vittorio, Serra, Luigi, Spinelli, Raffaele, Krishnamurthy, Balachander, Privacy Awareness about Information Leakage: Who Knows What About Me?, 2013, available at

Jonathan Mayer, FourthParty, available at

Jonathan Mayer, Tracking the Trackers: Early Results, Jul. 12, 2011, available at

Jonathan Mayer, Tracking the Trackers: To Catch a History Thief, Jul. 19, 2011, available at

Jonathan Mayer, Tracking the trackers: Where everybody knows your username, Oct. 11, 2011, available at

Mayer, Jonathan R., Mitchell, John C., Third-Party Web Tracking: Policy and Technology, 2012, available at

McDonald, A. M., & Cranor, L. F., A Survey of the Use of Adobe Flash Local Shared Objects to Respawn HTTP Cookies, CMU-CyLab-11-001 (2011), available at

Mowery, Keaton, Shacham, Hovav, Pixel Perfect: Fingerprinting Canvas in HTML5, 2012, available at

Mulazzani, Martin, Reschl, Philipp, Huber, Markus, Leithner, Manuel, Schrittwieser, Sebastian, and Weippl, Edgar, Fast and Reliable Browser Identification with JavaScript Engine Fingerprinting, 2013, available at

Nikiforakis, Nick, Kapravelos, Alexandros, Joosen, Wouter, Kruegel, Christopher, Piessens, Frank, Vigna, Giovanni, Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting, 2013, available at

Olejnik, Lukasz, Castelluccia, Claude, Janc, Artur, Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns, 2012, available at

Reisman et al, Cookies That Give You Away: Evaluating the Surveillance Implications of Web Tracking, 2014, available at

Ashkan Soltani, Shannon Canty, Quentin Mayo, Lauren Thomas, and Chris Jay Hoofnagle, Flash Cookies and Privacy, Aug. 10, 2009, available at:, accepted for publication at AAAI Spring Symposium on Intelligent Information Privacy Management, CodeX: The Stanford Center of Computers and Law.

Wills, Craig E., Tatar, Can, Understanding What They Do With What They Know, 2012, available at