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.
This research was partially supported by the grants from the Natural Science Foundation of China (No. 61070055, 91024032, 91124001); the National 863 High-tech Program (No. 2012AA010701, 2013AA013204); the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University( No. 11XNL010).
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Wang, M., Zhang, X., Meng, X. (2013). DiffR-Tree: A Differentially Private Spatial Index for OLAP Query. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_72
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DOI: https://doi.org/10.1007/978-3-642-38562-9_72
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