Secure Change-Point Detection for Time Series under Homomorphic Encryption
Authors: Federico Mazzone (University of Twente), Giorgio Micali (University of Twente), Massimiliano Pronesti (IBM Research Europe)
Volume: 2026
Issue: 2
Pages: 127–142
DOI: https://doi.org/10.56553/popets-2026-0040
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.
Keywords: time series, change-point detection, privacy, homomorphic encryption, ordinal patterns
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