Locally Private Parametric Methods for Change-Point Detection
The study investigates detecting a single change point in data while protecting privacy using local differential privacy. The authors derive improved accuracy bounds for non-private detection and propose two new algorithms based on randomized response and binary mechanisms for the private setting. They quantify how much privacy degrades performance, showing the error rate is reduced by a factor related to tanh^2(epsilon/2).