PrivFramework: A System for Configurable and Automated Privacy Policy Compliance

NeurIPS 2020 Workshop on Dataset Security and Curation Today's massive scale of data collection coupled with recent surges of consumer data leaks has led to increased attention towards data privacy and related risks. Conventional data privacy protection systems focus on reducing custodial risk...

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Bibliographic Details
Main Authors Khan, Usmann, Wang, Lun, Subramanian, Jithendaraa, Near, Joseph P, Song, Dawn
Format Journal Article
LanguageEnglish
Published 09.12.2020
Subjects
Computer Science - Cryptography and Security
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Summary:NeurIPS 2020 Workshop on Dataset Security and Curation Today's massive scale of data collection coupled with recent surges of consumer data leaks has led to increased attention towards data privacy and related risks. Conventional data privacy protection systems focus on reducing custodial risk and lack features empowering data owners. As an end user there are limited options available to specify and enforce one's own privacy preferences over their data. To address these concerns we present PrivFramework, a user-configurable frame-work for automated privacy policy compliance. PrivFramework allows data owners to write powerful privacy policies to protect their data and automatically enforces these policies against analysis programs written in Python. Using static-analysis PrivFramework automatically checks authorized analysis programs for compliance to user-defined policies.
DOI:10.48550/arxiv.2012.05291