Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory
Haoran Li, Wei Fan, Yulin Chen, Jiayang Cheng, Tianshu Chu, Xuebing, Zhou, Peizhao Hu, Yangqiu Song

TL;DR
This paper introduces a comprehensive privacy checklist grounded in Contextual Integrity theory, leveraging large language models to better detect privacy violations across social contexts and regulations.
Contribution
It formulates privacy as a reasoning problem, creating a broad checklist based on HIPAA and expert annotations, advancing human-centric privacy research.
Findings
Developed a privacy checklist covering HIPAA regulations.
Used LLMs to fully cover HIPAA's privacy norms.
Provided preliminary results to guide future context-centric privacy research.
Abstract
Privacy research has attracted wide attention as individuals worry that their private data can be easily leaked during interactions with smart devices, social platforms, and AI applications. Computer science researchers, on the other hand, commonly study privacy issues through privacy attacks and defenses on segmented fields. Privacy research is conducted on various sub-fields, including Computer Vision (CV), Natural Language Processing (NLP), and Computer Networks. Within each field, privacy has its own formulation. Though pioneering works on attacks and defenses reveal sensitive privacy issues, they are narrowly trapped and cannot fully cover people's actual privacy concerns. Consequently, the research on general and human-centric privacy research remains rather unexplored. In this paper, we formulate the privacy issue as a reasoning problem rather than simple pattern matching. We…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Privacy-Preserving Technologies in Data
MethodsSoftmax · Attention Is All You Need
