Differential Privacy
Differential privacy (DP) is a rigorous framework for ensuring data privacy in machine learning by adding carefully calibrated noise to model training processes. Current research focuses on improving the accuracy of DP models, particularly for large-scale training, through techniques like adaptive noise allocation, Kalman filtering for noise reduction, and novel gradient processing methods. This active area of research is crucial for enabling the responsible use of sensitive data in various applications, ranging from healthcare and finance to natural language processing and smart grids, while maintaining strong privacy guarantees.
Papers
Federated Learning on Riemannian Manifolds with Differential Privacy
Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra
Noiseless Privacy-Preserving Decentralized Learning
Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec, Laurent Massoulie, Rafael Pires, Rishi Sharma, Martijn de Vos
Privacy at a Price: Exploring its Dual Impact on AI Fairness
Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations
Fatima Ezzeddine, Omran Ayoub, Silvia Giordano
A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off
Stephen Meisenbacher, Nihildev Nandakumar, Alexandra Klymenko, Florian Matthes