PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy

arXiv:2404.03873v1 Announce Type: new
Abstract: Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it …

applications arxiv city cs.cr data differential privacy finance healthcare iot local may personal personal data privacy public series smart smart city under