Privacy Policy
Privacy policies, crucial for informing users about data handling practices and ensuring regulatory compliance, are the subject of intense research focusing on improving their clarity, accessibility, and automated analysis. Current efforts leverage techniques like natural language processing, large language models, and logic-based representations to enhance policy comprehension and compliance audits, while also exploring the interplay between privacy, fairness, and utility in machine learning models. This research is vital for promoting transparency and accountability in data practices, impacting both regulatory oversight and the development of trustworthy AI systems.
Papers
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell
When Evolutionary Computation Meets Privacy
Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang