9 citations · 53 across the 23 of their papers we have counts for
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cs.CR2022
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…
cs.LG2022★ 1 cited
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…
cs.DB2022★ 2 cited
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…