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【学术报告】Privacy Preserving Range Queries with Provable Security and Sublinear Scalability
文章来源:二室  |  发布时间:2016-04-27  |  【 】 【打印】 【关闭】  |  浏览:
报告人: 刘向阳 南京大学计算机科学与技术系教授 博导,长江学者。

报告时间:2016428日(周四) 下午15:30-17:00

报告地点:中科院信工所A3号楼1105号会议室

 

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 Amazon 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.

 
 
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