Secure Change-Point Detection for Time Series under Homomorphic Encryption

Fuente: arXiv
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Autores principales: Mazzone, Federico, Micali, Giorgio, Pronesti, Massimiliano
Formato: Preprint
Publicado: 2026
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author Mazzone, Federico
Micali, Giorgio
Pronesti, Massimiliano
author_facet Mazzone, Federico
Micali, Giorgio
Pronesti, Massimiliano
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_05865
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Secure Change-Point Detection for Time Series under Homomorphic Encryption
Mazzone, Federico
Micali, Giorgio
Pronesti, Massimiliano
Cryptography and Security
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.
title Secure Change-Point Detection for Time Series under Homomorphic Encryption
topic Cryptography and Security
url https://arxiv.org/abs/2601.05865