# Locally Private Parametric Methods for Change-Point Detection

arXiv: https://arxiv.org/abs/2602.13619 · HTML: https://mlsanity.com/paper/2602.13619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Local Differential Privacy and Its Applications: A Comprehensive Survey](https://mlsanity.com/paper/2008.03686/) (2020)
- [Local, Private, Efficient Protocols for Succinct Histograms](https://mlsanity.com/paper/1504.04686/) (2015)
- [Optimal Schemes for Discrete Distribution Estimation under Locally Differential Privacy](https://mlsanity.com/paper/1702.00610/) (2017)
- [The Structure of Optimal Private Tests for Simple Hypotheses](https://mlsanity.com/paper/1811.11148/) (2018)
- [Differentially Private Change-Point Detection](https://mlsanity.com/paper/1808.10056/) (2018)
- [Network change point localisation under local differential privacy](https://mlsanity.com/paper/2205.07144/) (2022)
- [Privately detecting changes in unknown distributions](https://mlsanity.com/paper/1910.01327/) (2019)
- [Locally private online change point detection](https://mlsanity.com/paper/2105.10675/) (2021)
- [Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits](https://mlsanity.com/paper/2405.05724/) (2024)
- [Locally Differentially-Private Randomized Response for Discrete Distribution Learning](https://mlsanity.com/paper/1811.12257/) (2018)

## Abstract (arXiv)

We study parametric change-point detection under local differential privacy, focusing on the offline localization of a single change point in a sequence of independent observations.~In the non-private setting, we derive improved finite-sample accuracy guarantees for a generalized likelihood-ratio estimator using martingale methods.~We establish finite-sample minimax lower bounds in both the non-private and locally private settings, showing that localization accuracy is fundamentally limited by the Chernoff information between the pre- and post-change distributions.~Guided by this, we design locally private CPD algorithms based on aggregated randomized response and binary mechanisms, which maximize the Chernoff information between the induced output distributions in low- and high-privacy regimes, respectively, and derive their finite-sample guarantees. Together, our upper and lower bounds quantify the statistical cost of privacy in change-point localization.~As part of our analysis, we also show that strong data processing inequality coefficients of R'enyi divergence and Jeffreys--R'enyi divergence of order infinity can be computed by restricting to pairs of input distributions with a common support of at most two symbols.~We corroborate our results through empirical evaluation on synthetic data.