Understanding the Privacy Implications of Adblock Plus's Acceptable Ads

Zafar, Ahsan; Sabir, Aafaq; Ahmed, Dilawer; Das, Anupam

doi:10.1145/3433210.3437536

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…We find that 376 browsers (89%) permit at least one request that should be blocked based on these lists. One possible explanation for this behavior is that browsers use different blocklists, unblock lists, or neither-again, consistent with prior work observing variations in blocking behavior [48,132]. Of the remaining 48 browsers, we look at browsers that load a majority of our test pages and make no requests on these lists and see that 17 browsers (4%) fit this criteria, including popular browsers like Firefox and Adblock Browser.…”

Section: Content Modificationsupporting

confidence: 66%

“…While we do not know exactly why these browsers block this content, we speculate that this could be an attempt to mitigate pervasive tracking from webpages. This also highlights the diversity in browsers' blocking behavior, indicating that different browsers use different anti-tracking and blocklist implementations [48,132]. Allowed requests.…”

Section: Content Modificationmentioning

confidence: 93%

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Not Your Average App: A Large-scale Privacy Analysis of Android Browsers

Pradeep

Feal

Gamba

et al. 2023

PoPETs

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The transparency and privacy behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one end, they can have access to (and can expose) a unique combination of sensitive user data, from users’ browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. However, on the other end, they also are in a unique position to protect users’ privacy by limiting data sharing with other parties by implementing ad-blocking features. In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we developed a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions. Overall, our study not only provides new insights into important yet overlooked considerations for browsers’ adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need context-specific analysis to reveal such privacy behaviors.

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Section: Content Modificationsupporting

confidence: 66%

Section: Content Modificationmentioning

confidence: 93%