Privacy Regulation and Protection in Machine Learning

Zheng Xu, Sewoong Oh, Salman Avestimehr, Tian Li, Niloofar Mireshghallah, Florian Tramèr

Workshop Type: Virtual

Keywords: privacy; regulation and policy; federated learning; differential privacy

Abstract:

Recent advances in artificial intelligence greatly benefit from data-driven machine learning methods that train deep neural networks with large scale data. The usage of data should be responsible, transparent, and comply with privacy regulations. This workshop aims to bring together industry and academic researchers, privacy regulators and legal, policy people to have a conversation on privacy research. We hope to (re)visit major privacy considerations from both technical and nontechnical perspectives through discussions with interdisciplinary discussions.

Topics of interest include, but are not limited to
Relationship of privacy regulation (such as GDPR, DMA) to machine learning;
Interpolation and explanation of data privacy;
Efficient methods for privacy preserving machine learning;
Federated learning for data minimization;
Differential privacy theory and practice;
Threat model and privacy attacks;
Encryption methods for machine learning;
Privacy in machine learning systems;
Privacy for large language models;
Relationship between privacy, transparency, auditability, verifiability;
Relationship between privacy, robustness, fairness etc.

Submission Number: 48