Towards Automated Regulation Analysis for Effective Privacy Compliance

Abstract

Privacy regulations are being introduced and amended around the globe to effectively regulate the processing of consumer data. These regulations are often analyzed to fulfill compliance mandates and to aid the design of practical systems that improve consumer privacy. However, at present, this is done manually, making the task error-prone, while also incurring significant time, effort, and cost for companies. This paper describes the design and implementation of ARC, a framework that transforms unstructured and complex regulatory text into a structured representation, the ARC tuple(s), which can be queried to assist in the analysis and understanding of regulations. We demonstrate ARC’s effectiveness in extracting three forms of tuples with a high F-1 score (avg. 82.1% across all three) using four major privacy regulations: CCPA, GDPR, VCDPA, and PIPEDA. We then build ARCBert that identifies semantically similar phrases across regulations, enabling compliance analysts to identify common requirements. We run ARC on 16 additional privacy regulations and identify 1,556 ARC tuples and clusters of semantically similar phrases. Finally, we extend ARC to evaluate the compliance of privacy policies by comparing it against the disclosure requirements in the four regulations. Our empirical evaluation with the privacy policies of S&P 500 companies finds 476 missing disclosures, which when manually validated, result in 71.05% true positives, as well as the discovery of 288 additional missing disclosures from the partial matches identified by ARC.