AI RESEARCH
Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms
arXiv CS.LG
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ArXi:2603.26227v1 Announce Type: cross We study privacy-preserving sparse linear regression in the high-dimensional regime, focusing on the LASSO estimator. We analyze two widely used mechanisms for differential privacy: output perturbation, which injects noise into the estimator, and objective perturbation, which adds a random linear term to the loss function. Using approximate message passing (AMP), we characterize the typical behavior of these estimators under random design and privacy noise.