9 citations · 53 across the 23 of their papers we have counts for
23 papers
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Xinpeng Ling, Jie Fu, Kuncan Wang +2
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw…
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…
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
Jie Fu, Zhili Chen, Xiao Han
Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private inform…
LDP-FPMiner: FP-Tree Based Frequent Itemset Mining with Local Differential Privacy
Zhili Chen, Jiali Wang
Data aggregation in the setting of local differential privacy (LDP) guarantees strong privacy by providing plausible deniability of sensitive data. Existing works on this issue mos…
DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels
Shun Zhang, Pengfei Lan, Benfei Duan +3
The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation…
Utility-efficient Differentially Private K-means Clustering based on Cluster Merging
Tianjiao Ni, Minghao Qiao, Zhili Chen +2
Differential privacy is widely used in data analysis. State-of-the-art -means clustering algorithms with differential privacy typically add an equal amount of noise to centroids…