Web tracking

Summary

Web tracking is the practice by which operators of websites and third parties collect, store and share information about visitors’ activities on the World Wide Web. Analysis of a user's behaviour may be used to provide content that enables the operator to infer their preferences and may be of interest to various parties, such as advertisers. Web tracking can be part of visitor management. The uses of web tracking include the following: Advertising companies actively collect information about users and make profiles that are used to individualize advertisements. User activities include websites visited, watched videos, interactions on social networks, and online transactions. Websites like Netflix and YouTube collect information about what shows users watch, which helps them suggest more shows that they might like. Search engines like Google will keep a record of what users search for, which could help them suggest more relevant searches in the future. Law enforcement agencies may use web tracking to spy on individuals and solve crimes. Web analytics focuses more on the performance of a website as a whole. Web tracking will give insight on how a website is being used and see how long a user spends on a certain page. This can be used to see who may have the most interest in the content of the website. Usability tests is the practice of testing how easy a design is to use. Users are observed as they complete tasks. This would help identify usability problems with a website's design so they can be fixed for easier navigation. Every device connected to the Internet is assigned a unique IP address, which is needed to enable devices to communicate with each other. With appropriate software on the host website, the IP address of visitors to the site can be logged and can also be used to determine the visitor’s geographical location. Logging the IP address can, for example, monitor if a person voted more than once, as well as their viewing pattern. Knowing the visitor's location indicates, besides other things, the country.

Official source

https://en.wikipedia.org/wiki/Web_tracking

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Related publications (2)

Related concepts (19)

Related courses (20)

Related lectures (47)

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstrate the power of metadata analysis. We illustrate ways in which we can use metadata analysis to protect privacy, by addressing two problems in the areas of network and web privacy.In the first problem, we study recently standardized protocols such as encrypted DNS and QUIC. We show how metadata analysis can be used by adversaries to perform website-fingerprinting attacks against these protocols and to infer websites visited by a user, thereby compromising their privacy. We use the insights from our analysis to identify the requirements for developing effective countermeasures and to improve the resistance of these protocols to website fingerprinting. We find that hiding metadata is challenging.In the second problem, we address the issue of online advertising and tracking services (ATS) that are constantly evolving to evade privacy protections established by browser vendors. We demonstrate how the very fact of metadata being hard to hide can be useful to defenders. Defenders can use metadata analysis to build ATS-detection systems that are more robust against adversarial evasion by capturing behavioral metadata of ATS. We use our findings to detect and counter the emergence of tracking via first-party cookies.

EPFL2022

WEBGRAPH: Capturing Advertising and Tracking Information Flows for Robust Blocking

Carmela González Troncoso, Sandra Deepthy Siby

Users rely on ad and tracker blocking tools to protect their privacy. Unfortunately, existing ad and tracker blocking tools are susceptible to mutable advertising and tracking content. In this paper, we first demonstrate that a state-of-the-art ad and tracker blocker, ADGRAPH, is susceptible to such adversarial evasion techniques that are currently deployed on the web. Second, we introduce WEBGRAPH, the first ML-based ad and tracker blocker that detects ads and trackers based on their action rather than their content. By featurizing the actions that are fundamental to advertising and tracking information flows - e.g., storing an identifier in the browser or sharing an identifier with another tracker - WEB GRAPH performs nearly as well as prior approaches, but is significantly more robust to adversarial evasions. In particular, we show that WEBGRAPH achieves comparable accuracy to ADGRAPH, while significantly decreasing the success rate of an adversary from near-perfect for ADGRAPH to around 8% for WEBGRAPH. Finally, we show that WEB GRAPH remains robust to sophisticated adversaries that use adversarial evasion techniques beyond those currently deployed on the web.

USENIX ASSOC2022