Title: Privacy Preserving Range Queries with Provable Security and Sublinear Scalability
报告人: Alex Liu教授，密西根州立大学
时间: 2016.5.5 10:00AM
地点: 教研楼 2楼会议室
Abstract: In this talk, I will talk about privacy preserving range queries. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services have been increasingly outsourced to clouds such as 亚马逊 EC2. However, privacy has been the key road block to cloud computing. On one hand, to leverage the computing and storage capability offered by clouds, we need to store data on clouds. On the other hand, due to many reasons, we may not fully trust the clouds for data privacy. This paper concerns the problem of privacy preserving range query processing on clouds. Although some prior privacy preserving range query processing schemes have been proposed in the past, none of them can achieve both provable security and sublinear scalability. In this work, we propose the first range query processing scheme that achieves both. We implemented and evaluated our scheme on a real world data set. The experimental results show that our scheme can efficiently support real time range queries with 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 for real time applications.
Short Bio: Alex X. Liu received his Ph.D. degree in Computer Science from The University of Texas at Austin in 2006. He received the IEEE & IFIP William C. Carter Award in 2004, the National Science Foundation CAREER Award in 2009, and the Michigan State University Withrow Distinguished Scholar Award in 2011. His special research interests are in networking, security, and privacy. His general research interests include computer systems, distributed computing, and dependable systems.