报告人：叶青青

腾讯会议：119-946-136

报告时间：2023-08-10 15:00

Abstract: A time series is a sequence of values indexed in a discrete time order. Time series analysis has numerous applications big data analytics. However, many time series originate from personal data, such as biosensors in telecare, IoT sensors in smart home, and trajectories for mobility tracking in COVID-19 pandemic. Directly releasing them to the public can cause privacy infringement. To address this issue, many privacy-preserving time series publishing techniques have been proposed, among which differential privacy (DP) provides rigirous privacy guarantee for individual data and has been regarded as a golden standard for privacy protection. This talk first introduces the rationale of DP model, then presents our recent work on time series data release with DP, and finally identifies some open problems and research directions in this community.

Bio: Qingqing Ye is an Assistant Professor in the Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University. She received her PhD degree from Renmin University of China in 2020. Her research interests include data privacy and security, and adversarial machine learning. She has published a series of flagship conference papers and top-tier journal papers in related areas, including IEEE S&P, SIGMOD, VLDB, ICDE, INFOCOM, NuerIPS, TKDE, TDSC, TIFS, etc.

邀请人：唐朋

审核人：魏普文