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.
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Papers - Page 21
November 10, 2023
November 7, 2023
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models
Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu SongCausal Discovery Under Local Privacy
Rūta Binkytė, Carlos Pinzón, Szilvia Lestyán, Kangsoo Jung, Héber H. Arcolezi, Catuscia Palamidessi
November 6, 2023
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
David Sánchez, Najeeb Jebreel, Krishnamurty Muralidhar, Josep Domingo-Ferrer, Alberto Blanco-JusticiaSoK: Memorisation in machine learning
Dmitrii Usynin, Moritz Knolle, Georgios KaissisDP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Huifa Li, Jie Fu, Zhili Chen, Xiaomin Yang, Haitao Liu, Xinpeng Ling
November 1, 2023
October 31, 2023
October 30, 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye, Reza Shokri, Weijie J. SuPrivacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Yiwei Li, Chien-Wei Huang, Shuai Wang, Chong-Yung Chi, Tony Q. S. QuekPrivacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo, Alan King, Yi Zhou, Keith Houck, Ambrish Rawat, Mark Purcell, Naoise Holohan+6
October 29, 2023
October 24, 2023
October 23, 2023