12 citations · 14 across the 9 of their papers we have counts for
4 papers · 1 filter
Protecting K-Nearest Neighbor Queries from Location Inference Attacks
Zhiyu Sun, Jie Fu, Xinpeng Ling +2
The k-nearest neighbor query (kNNQ) is a core component of modern location-based services (LBS) and has been widely adopted in popular features such as ``people nearby''. However,…
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling +6
In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de…
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
Xinpeng Ling, Jie Fu, Kuncan Wang +3
Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persiste…
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing
Jie Fu, Zhili Chen, XinPeng Ling
Differential privacy (DP) provides a formal privacy guarantee that prevents adversaries with access to machine learning models from extracting information about individual training…