報告題目: Privacy-preserving Data Collection: Challenge and Solution

報告時間：6月25号上午10點

報告地點：計算機學院B404報告廳

報告人：秦湛博士

報告人簡介： Dr. Zhan Qin is currently an Assistant Professor of Electrical and Computer Engineering at The University of Texas at San Antonio. He received the PhD degree from the Computer Science and Engineering department at State University of New York at Buffalo in 2017. His current research interests include privacy-preserving data collection and analysis, secure computation outsourcing, and cyber-physical security in the context of Internet of Things. His works explore and develop novel security sensitive algorithms and protocols for computation and communication on Cloud and IoT devices.

報告摘要：Local Differential Privacy (LDP) is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, it allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. Specifically, each participant perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. The data analyst never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. No existing work tackles more complex data mining tasks such as heavy hitter discovery over set-valued data. In this talk, I will introduce the challenges of heavy hitter mining under LDP, then present my recent work LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. In addition, my works on privacy-preserving social network generation and other ongoing research projects will also be briefly introduced.