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
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts
Zhili Shen, Zihang Xi, Ying He, Wei Tong, Jingyu Hua, Sheng Zhong
Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images
Shivank Garg, Manyana Tiwari
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing
Viet Vo, Thusitha Dayaratne, Blake Haydon, Xingliang Yuan, Shangqi Lai, Sharif Abuadbba, Hajime Suzuki, Carsten Rudolph
Demystifying Legalese: An Automated Approach for Summarizing and Analyzing Overlaps in Privacy Policies and Terms of Service
Shikha Soneji, Mitchell Hoesing, Sujay Koujalgi, Jonathan Dodge
Embedding Privacy in Computational Social Science and Artificial Intelligence Research
Keenan Jones, Fatima Zahrah, Jason R. C. Nurse