G old C oin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory (2024)

Abstract: Privacy issues arise prominently during the inappropriate transmission of information between entities. Existing research primarily studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns, while neglecting that privacy is not an isolated, context-free concept limited to traditionally sensitive data (e.g., social security numbers), but intertwined with intricate social contexts that complicate the identification and analysis of potential privacy violations. The advent of Large Language Models (LLMs) offers unprecedented opportunities for incorporating the nuanced scenarios outlined in privacy laws to tackle these complex privacy issues. However, the scarcity of open-source relevant case studies restricts the efficiency of LLMs in aligning with specific legal statutes. To address this challenge, we introduce a novel framework, GoldCoin, designed to efficiently ground LLMs in privacy laws for judicial assessing privacy violations. Our framework leverages the theory of contextual integrity as a bridge, creating numerous synthetic scenarios grounded in relevant privacy statutes (e.g., HIPAA), to assist LLMs in comprehending the complex contexts for identifying privacy risks in the real world. Extensive experimental results demonstrate that GoldCoin markedly enhances LLMs’ capabilities in recognizing privacy risks across real court cases, surpassing the baselines on different judicial tasks.

## introduction privacy violations happen through improper information transmission, including the disclosure of personally identifiable information, inappropriate data collection, and unauthorized access, all background: jane, a 45-year-old woman, visited her primary care physician, dr. smith, for her annual checkup. during the appointment, dr. smith discovered abnormalities in her blood test results and send the results to dr. adams, for specialist diagnostic assessment and treatment planning. ## method legal judgments on privacy violations typically involve two tasks @xcite : (1) applicability, assessing whether the privacy law l applies to the case background s; and (2) compliance, determining if the transmission described in s compliant with l. in this section, we introduce gold-coin, which applies contextual integrity theory to generate synthetic cases. after postprocessing the instances, we instruction-tune llms and evaluate their performance in the above two tasks. the overview of our pipeline is shown in figure 3 . ## goldcoin-hipaa dataset overview in this section, we apply our framework to the hipaa privacy rule as a case study and introduce the goldcoin-hipaa dataset. statistics of the dataset are provided in ## experiment in this section, we conduct extensive experiments to demonstrate the efficacy of goldcoin in grounding llms into real-world privacy laws. ## conclusion in this paper, we introduce goldcoin, a pioneering framework that leverages the contextual integrity theory to effectively apply privacy laws to privacy violation detection. specifically, we practice the hipaa privacy rule and build synthetic cases for aligning llms. our experimental results demonstrate that this approach significantly enhances models' capability to assess legal relevance and pinpoint privacy risks, providing a novel perspective for the integration of privacy legislation within llms. in the future, this generation and alignment method could be extended to other privacy laws such as gdpr and coppa, or general legal domains. we hope our goldcoin sheds light on the development of legal llms.