Locally Private Parametric Methods for Change-Point Detection
Read the original on arXiv Machine Learning →The paper investigates parametric change‑point detection under local differential privacy, concentrating on offline localization of a single change point in independent observations. It derives improved finite‑sample accuracy guarantees for a generalized likelihood‑ratio estimator using martingale methods and establishes finite‑sample minimax lower bounds in both non‑private and locally private settings, linking localization accuracy to Chernoff information. Guided by these bounds, the authors design locally private algorithms based on aggregated randomized response and binary mechanisms, optimize Chernoff information in different privacy regimes, and provide finite‑sample guarantees, while also computing strong data‑processing inequality coefficients for certain divergences and validating the results empirically on synthetic data.
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