PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps
Yuanchun Li, Fanglin Chen, Toby Jia-Jun Li, Yao Guo, Gang Huang, Matthew Fredrikson, Yuvraj Agarwal, and Jason I Hong
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
September 2017
Smartphone app developers often access and use privacy-sensitive data to create apps with rich and meaningful interactions. However, it can be challenging for auditors and end-users to know what granularity of data is being used and how, thereby hindering assessment of potential risks. Furthermore, developers lack easy ways of offering transparency to users regarding how personal data is processed, even if their intentions are to make their apps more privacy friendly. To address these challenges, we introduce PrivacyStreams, a functional programming model for accessing and processing personal data as a stream. PrivacyStreams is designed to make it easy for developers to make use of personal data while simultaneously making it easier to analyze how that personal data is processed and what granularity of data is actually used. We present the design and implementation of PrivacyStreams, as well as several user studies and experiments to demonstrate its usability, utility, and support for privacy.
