AI RESEARCH
Near-Optimal Private Tests for Simple and MLR Hypotheses
arXiv CS.LG
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ArXi:2601.21959v2 Announce Type: replace-cross We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio conditions. Our mechanism is based on a private mean estimator with data-driven clamping bounds, whose population risk matches the private minimax rate up to logarithmic factors.