DiffR-Tree: a differentially private spatial index for OLAP query

Authors:
Miao Wang;Xiaojian Zhang;Xiaofeng Meng
Affiliations:
School of Information, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China,School of Computer and Information Engineering, Henan University of Economics and Law, China;School of Information, Renmin University of China, Beijing, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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Abstract

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.