Privacy Attacks on Matrix Profiles via Reconstruction Techniques
Authors: Haoying Zhang (INSA CVL, Univ. d'Orléans, Inria), Nicolas Anciaux (Inria, INSA CVL, Univ. Paris Saclay), Benjamin Nguyen (INSA CVL, Univ. d'Orléans, Inria), Fabien Girard (ENSTA), Jose Maria de Fuentes (Univ. Carlos III de Madrid), Adrien Boiret (INSA CVL, Univ. d'Orléans, Inria)
Volume: 2026
Issue: 1
Pages: 65–86
DOI: https://doi.org/10.56553/popets-2026-0005
Abstract: Matrix Profile (MP) is a data mining structure increasingly used for time series analysis in both academic and industrial contexts. Given its application to sensitive domains such as healthcare or energy monitoring, it is crucial to examine associated privacy risks, especially since MPs are often shared or processed in untrusted environments like the cloud. While recent studies suggest that MPs offer some privacy protection, this assumption remains largely untested. This paper analyzes the privacy risks of MP publication through the lens of EU data protection law, focusing on singling-out, linkability, and inference risks. We introduce a reconstruction technique based on constraint optimization, capable of recovering approximate original time series from their MPs, leading to severe privacy attacks. Experiments on real-world datasets reveal vulnerabilities to all attack types, with reconstructed series reaching up to 0.99 Pearson Correlation with the original.
Keywords: Privacy attacks, Reconstruction, Matrix Profile
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