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
Privacy in Metalearning and Multitask Learning: Modeling and Separations
Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan Ullman
The Impact of Generalization Techniques on the Interplay Among Privacy, Utility, and Fairness in Image Classification
Ahmad Hassanpour, Amir Zarei, Khawla Mallat, Anderson Santana de Oliveira, Bian Yang
Training Data Reconstruction: Privacy due to Uncertainty?
Christina Runkel, Kanchana Vaishnavi Gandikota, Jonas Geiping, Carola-Bibiane Schönlieb, Michael Moeller
Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
Dong Chen, Alice Dethise, Istemi Ekin Akkus, Ivica Rimac, Klaus Satzke, Antti Koskela, Marco Canini, Wei Wang, Ruichuan Chen
On the Privacy, Security, and Trustworthy for Distributed Wireless Large AI Model (WLAM)
Zhaohui Yang, Wei Xu, Le Liang, Yuanhao Cui, Zhijin Qin, Merouane Debbah
Federated Analytics in Practice: Engineering for Privacy, Scalability and Practicality
Harish Srinivas, Graham Cormode, Mehrdad Honarkhah, Samuel Lurye, Jonathan Hehir, Lunwen He, George Hong, Ahmed Magdy, Dzmitry Huba, Kaikai Wang, Shen Guo, Shoubhik Bhattacharya