IDPFilter: Mitigating Interdependent Privacy Issues in Third-Party Apps
Shuaishuai Liu, Gergely Bicz\'ok

TL;DR
This paper investigates interdependent privacy issues in third-party apps, analyzing permission structures, characterizing app behaviors, and proposing IDPFilter, a platform-agnostic API to mitigate privacy risks by filtering collateral data collection.
Contribution
It introduces IDPFilter, a novel API designed to reduce interdependent privacy risks in third-party apps, supported by analysis, principles, and a prototype implementation.
Findings
Identified permissions causing interdependent privacy issues
Characterized app permission request patterns
Demonstrated IDPFilter's effectiveness in filtering collateral data
Abstract
Third-party applications have become an essential part of today's online ecosystem, enhancing the functionality of popular platforms. However, the intensive data exchange underlying their proliferation has increased concerns about interdependent privacy (IDP). This paper provides a comprehensive investigation into the previously underinvestigated IDP issues of third-party apps. Specifically, first, we analyze the permission structure of multiple app platforms, identifying permissions that have the potential to cause interdependent privacy issues by enabling a user to share someone else's personal data with an app. Second, we collect datasets and characterize the extent to which existing apps request these permissions, revealing the relationship between characteristics such as the respective app platform, the app's type, and the number of interdependent privacy-related permissions it…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Advanced Malware Detection Techniques · Mobile and Web Applications
