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Conference Proceeding

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Recent rapid development of sensor technology has allowed massive time series data to be collected and set foundation for the development of data-driven services and applications. During the process, data sharing is often required to allow modelers to perform specific time series data mining tasks based on the need of data owner. The high resolution of time series data brings new challenges in privacy protection, as meaningful information in high-resolution data shifts from concrete point values to shape-based patterns. Numerous research efforts have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused by a malicious modeler. However, the privacy issue for time series patterns is surprisingly seldom explored in privacy-preserving literature. In this work, we consider a new privacy preserving problem: preventing malicious inference on long shape-based patterns while preserving short segment information to maintain utility task performance. To mitigate the challenge, we investigate an alternative approach by sharing Matrix Profile (MP), a versatile data structure that supports many time series data mining tasks. We found that while MP can prevent the concrete shape leakage, the canonical correlation in MP index can still reveal the location of sensitive long pattern information. Based on this observation, we design two attacks named Location Attack and Entropy Attack to extract the pattern location from MP. To further protect MP from these two attacks, we propose a Privacy-Aware Matrix Profile (PMP) via perturbing the local correlation and breaking the canonical correlation in MP index vector. We evaluate our proposed PMP against baseline noise-adding methods through quantitative analysis and real-world case study to show the effectiveness of the proposed method. Our source code is available at


Copyright © 2023 by SIAM

Publication Title

Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)





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