PrivGuard: Privacy Regulation Compliance Made Easier

Authors: 

Lun Wang, UC Berkeley; Usmann Khan, Georgia Tech; Joseph Near, University of Vermont; Qi Pang, Zhejiang University; Jithendaraa Subramanian, NIT Tiruchirappalli; Neel Somani, UC Berkeley; Peng Gao, Virginia Tech; Andrew Low and Dawn Song, UC Berkeley

Abstract: 

Continuous compliance with privacy regulations, such as GDPR and CCPA, has become a costly burden for companies from small-sized start-ups to business giants. The culprit is the heavy reliance on human auditing in today's compliance process, which is expensive, slow, and error-prone. To address the issue, we propose PrivGuard, a novel system design that reduces human participation required and improves the productivity of the compliance process. PrivGuard is mainly comprised of two components: (1) PrivAnalyzer, a static analyzer based on abstract interpretation for partly enforcing privacy regulations, and (2) a set of components providing strong security protection on the data throughout its life cycle. To validate the effectiveness of this approach, we prototype PrivGuard and integrate it into an industrial-level data governance platform. Our case studies and evaluation show that PrivGuard can correctly enforce the encoded privacy policies on real-world programs with reasonable performance overhead.

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BibTeX
@inproceedings {277104,
title = {{PrivGuard}: Privacy Regulation Compliance Made Easier},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
address = {Boston, MA},
url = {https://www.usenix.org/conference/usenixsecurity22/presentation/wang-lun},
publisher = {USENIX Association},
month = aug,
}