COOKIEGRAPH: Understanding and Detecting First-Party Tracking Cookies

Authors: Shaoor Munir, Sandra Siby, Umar Iqbal, Steven Englehardt, Zubair Shafiq, Carmela Troncoso
Published: 2022-08-25 | Updated: 2023-11-27

Source: https://arxiv.org/abs/2208.12370

PDF: https://arxiv.org/pdf/2208.12370

Labels Predicted by AI

プライバシー侵害 ユーザー活動解析 サイバーセキュリティ

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked. This study reveals that first-party cookies are used to store and exfiltrate identifiers to known trackers even when third-party cookies are blocked. As opposed to third-party cookie blocking, outright first-party cookie blocking is not practical because it would result in major functionality breakage. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.20 state-of-the-art CookieBlock approach by 17.75 fully robust against cookie name manipulation while CookieBlock’s acuracy drops by 15.68 32 that CookieGraph does not cause any major breakage on these sites. Our deployment of CookieGraph shows that first-party tracking cookies are used on 93.43 cookies are set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.