Secure Change-Point Detection for Time Series under Homomorphic Encryption

TL;DR

This paper presents the first method for detecting change-points in encrypted time series data using homomorphic encryption, enabling privacy-preserving analysis without significant accuracy loss.

Contribution

It introduces a novel homomorphic encryption-based approach for change-point detection that maintains high utility and efficiency on large datasets.

Findings

Detects change-points accurately on encrypted data

Processes one million points in approximately 3 minutes

Maintains utility comparable to plaintext methods

Abstract

We introduce the first method for change-point detection on encrypted time series. Our approach employs the CKKS homomorphic encryption scheme to detect shifts in statistical properties (e.g., mean, variance, frequency) without ever decrypting the data. Unlike solutions based on differential privacy, which degrade accuracy through noise injection, our solution preserves utility comparable to plaintext baselines. We assess its performance through experiments on both synthetic datasets and real-world time series from healthcare and network monitoring. Notably, our approach can process one million points within 3 minutes.