As third-party cookie blocking is becoming the norm in mainstream web browsers, advertisers and trackers have started to use first-party cookies for tracking. To understand this phenomenon, we conduct a differential measurement study with versus without third-party cookies. We find 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, first-party cookie blocking is not practical because it would result in major breakage of website functionality. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect and block first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.18% accuracy, outperforming the state-of-the-art CookieBlock by 17.31%. We show that CookieGraph is robust against cookie name manipulation, while CookieBlock's accuracy drops by 15.87%. While blocking all first-party cookies results in major breakage on 32% of the sites with SSO logins, and CookieBlock reduces it to 10%, we show that CookieGraph does not cause any major breakage on these sites. Our deployment of CookieGraph shows that first-party tracking cookies are used on 89.86% of the top-million websites. We find that 96.61% of these first-party tracking cookies are in fact ghostwritten by third-party scripts embedded in the first-party context. We also find evidence of first-party tracking cookies being set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.
CITATION STYLE
Munir, S., Siby, S., Iqbal, U., Englehardt, S., Shafiq, Z., & Troncoso, C. (2023). CookieGraph: Understanding and Detecting First-Party Tracking Cookies. In CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (pp. 3490–3504). Association for Computing Machinery, Inc. https://doi.org/10.1145/3576915.3616586
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