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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 negative answers to this question. Initially, we provide studies affirming the current privacy analysis is indeed tight in a general sense. However, when we specifically study the hyper-parameter tuning problem, such tightness no longer holds. This is first demonstrated by applying privacy audit on the tuning process. Our findings underscore a substantial gap between the current theoretical privacy bound and the empirical bound derived even under the strongest audit setup. The gap found is not a fluke. Our subsequent study provides an improved privacy result for private hyper-parameter tuning due to its distinct properties. Our privacy results are also more generalizable compared to prior analyses that are only easily applicable in specific setups.
Xiang et al. (Tue,) studied this question.