Towards Privacy-Preserving Social-Media SDKs on Android


Haoran Lu, Yichen Liu, Xiaojing Liao, and Luyi Xing, Indiana University Bloomington


Integration of third-party SDKs are essential in the development of mobile apps. However, the rise of in-app privacy threat against mobile SDKs— called cross-library data harvesting (XLDH), targets social media/platform SDKs (called social SDKs) that handles rich user data. Given the widespread integration of social SDKs in mobile apps, XLDH presents a significant privacy risk, as well as raising pressing concerns regarding legal compliance for app developers, social media/platform stakeholders, and policymakers. The emerging XLDH threat, coupled with the increasing demand for privacy and compliance in line with societal expectations, introduces unique challenges that cannot be addressed by existing protection methods against privacy threats or malicious code on mobile platforms. In response to the XLDH threats, in our study, we generalize and define the concept of privacy-preserving social SDKs and their in-app usage, characterize fundamental challenges for combating the XLDH threat and ensuring privacy in design and utilizaiton of social SDKs. We introduce a practical, clean-slate design and end-to-end systems, called PESP, to facilitate privacy-preserving social SDKs. Our thorough evaluation demonstrates its satisfactory effectiveness, performance overhead and practicability for widespread adoption.

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