How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning

arXiv:2402.13087v1 Announce Type: new
Abstract: We study the problem of guaranteeing Differential Privacy (DP) in hyper-parameter tuning, a crucial process in machine learning involving the selection of the best run from several. Unlike many private algorithms, including the prevalent DP-SGD, the privacy implications of tuning remain insufficiently understood. Recent works propose a generic private solution for the tuning process, yet a fundamental question still persists: is the current privacy bound for this solution tight?
This paper contributes both positive and …

abstract algorithms arxiv cs.cr cs.lg differential differential privacy leak machine machine learning privacy process study type