文献类型：会议论文

英文题名：DiffR-Tree: A differentially private spatial index for OLAP query

作者：Wang, Miao[1]; Zhang, Xiaojian[1,2]; Meng, Xiaofeng[1]

第一作者：Wang, Miao

机构：[1] School of Information, Renmin University of China, Beijing, China; [2] School of Computer and Information Engineering, Henan University of Economics and Law, China

第一机构：School of Information, Renmin University of China, Beijing, China

会议论文集：Web-Age Information Management - 14th International Conference, WAIM 2013, Proceedings

会议日期：June 14, 2013 - June 16, 2013

会议地点：Beidaihe, China

语种：英文

外文关键词：Data warehouses - Decision trees - Forestry - Information management

摘要：Differential privacy has emerged as one of the most promising privacy models for releasing the results of statistical queries on sensitive data, with strong privacy guarantees. Existing works on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the spatial OLAP queries, which combines GIS and OLAP queries at the same time. We employ a differentially private R-tree(DiffR-Tree) to help spatial OLAP queries. In our method, several steps need to be carefully designed to equip the spatial data warehouse structure with differential privacy requirements. Our experiments results demonstrate the efficiency of our spatial OLAP query index structure and the accuracy of answering queries. ? 2013 Springer-Verlag Berlin Heidelberg.