报告题目：Privacy Preserving Range Queries with Provable Security andSublinear Scalability
报告日期及时间：2016年05月04日星期三，上午10：00
报告地点：计算机学院大楼B-403
报告人：AlexX. Liu
报告人国籍：Chinese
报告人单位：Michigan State University, USA
报告人简介：AlexX. Liu received his Ph.D. degree in Computer Science from The University ofTexas at Austin in 2006. He received the IEEE & IFIP William C. CarterAward in 2004, the National Science Foundation CAREER Award in 2009, and theMichigan State University Withrow Distinguished Scholar Award in 2011. Hisspecial research interests are in networking, security, and privacy. Hisgeneral research interests include computer systems, distributed computing, anddependable systems.
报告摘要：In this talk, I will talk aboutprivacy preserving range queries. Driven by lower cost, higher reliability,better performance, and faster deployment, data and computing services havebeen increasingly outsourced to clouds such as Amazon EC2. However, privacy hasbeen the key road block to cloud computing. On one hand, to leverage thecomputing and storage capability offered by clouds, we need to store data onclouds. On the other hand, due to many reasons, we may not fully trust theclouds for data privacy. This paper concerns the problem of privacy preservingrange query processing on clouds. Although some prior privacy preserving rangequery processing schemes have been proposed in the past, none of them canachieve both provable security and sublinear scalability. In this work, wepropose the first range query processing scheme that achieves both. Weimplemented and evaluated our scheme on a real world data set. The experimentalresults show that our scheme can efficiently support real time range querieswith strong privacy protection. For example, for a set of 10,000 data items,the time for processing a query is only 0.062 milliseconds, which is enough forreal time applications.
邀请人：王骞教授
