Abstract

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.

Bibtex

@article{Li:2017:PET:3139486.3130941,
    author = "Li, Yuanchun and Chen, Fanglin and Li, Toby Jia-Jun and Guo, Yao and Huang, Gang and Fredrikson, Matthew and Agarwal, Yuvraj and Hong, Jason I",
    pages = "76:1----76:26",
    journal = "Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.",
    title = "PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps",
    year = "2017",
    month = "09",
    volume = "1",
    doi = "10.1145/3130941"
}

Plain Text

Yuanchun Li, Fanglin Chen, Toby Jia-Jun Li, Yao Guo, Gang Huang, Matthew Fredrikson, Yuvraj Agarwal, and Jason I Hong. Privacystreams: enabling transparency in personal data processing for mobile apps. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1:76:1—–76:26, 09 2017. doi:10.1145/3130941.