  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.
