Differentially Private Log-Location-Scale Regression Using Functional Mechanism

arXiv:2404.08715v1 Announce Type: cross
Abstract: This article introduces differentially private log-location-scale (DP-LLS) regression models, which incorporate differential privacy into LLS regression through the functional mechanism. The proposed models are established by injecting noise into the log-likelihood function of LLS regression for perturbed parameter estimation. We will derive the sensitivities utilized to determine the magnitude of the injected noise and prove that the proposed DP-LLS models satisfy $\epsilon$-differential privacy. In addition, we will conduct simulations and case studies to evaluate the …

abstract article arxiv cs.cr cs.lg differential differential privacy function functional likelihood location noise privacy regression scale stat.ap stat.ml through type will