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.

翻译：本文提出了首个针对加密时间序列的变点检测方法。该方法采用CKKS同态加密方案，能够在不解密数据的情况下检测统计特性（如均值、方差、频率）的突变。与基于差分隐私的解决方案通过噪声注入降低精度不同，我们的方案保持了与明文基线相当的实用性。我们通过在合成数据集以及来自医疗健康和网络监控的真实时间序列上进行实验来评估其性能。值得注意的是，我们的方法能在3分钟内处理一百万个数据点。