PrivGuard: Privacy Regulation Compliance Made Easier


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


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.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {277104,
title = {{PrivGuard}: Privacy Regulation Compliance Made Easier},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
address = {Boston, MA},
url = {},
publisher = {USENIX Association},
month = aug,