TSEditor: Interactive Time Series Editing for Privacy Preservation

Publishing time series datasets raises substantial privacy concerns, as the underlying patterns (e.g., trends, values) can lead to the disclosure of individual identification. Mitigating these concerns remains challenging due to difficulties in pinpointing specific privacy-leaking patterns and protecting them without significantly compromising the analytical utility of the published data. Existing methods remain vulnerable to identity attacks utilizing diverse temporal patterns and may compromise data utility for subsequent analytical tasks. To address these limitations, we collaborated with domain experts to summarize a taxonomy of privacy risks in time series data and developed TSEditor, an interactive editing system. TSEditor integrates coordinated views for multi-perspective analysis of privacy risks and introduces six editing operations for targeted modifications, providing visual feedback. We demonstrate the effectiveness and usability of TSEditor through two case studies, an expert interview, a model evaluation, and a user study.