Target perturbation-based privacy protection for crowdsensing under heterogeneous conditions
JIANG Wei-jin
WANG Hai-juan
LI Yi-xiao
JIANG Yi-rong
Abstract：In federated learning architectures for mobile crowdsensing,users face the risk of privacy leakage.Existing differential privacy-based schemes suffer from a loss of local model training accuracy due to gradient clipping,especially in heterogeneous environments.To address these issues,our paper first employs federated stochastic principal component analysis to reduce the dimensionality of the data.Subsequently,the objective function perturbed by rényi-differential privacy is used to replace the gradient for updates.Then,Bregman divergence is introduced as a regularization term to update the loss function,constraining the deviation between the local and global models.Experimental results demonstrate that the proposed method achieves higher accuracy and convergence precision compared to several existing approaches.
Keywords：mobile crowdsensingprivacy protectionrényi-differential privacyheterogeneity
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：12( 2374-2385 )