Differentially Private Spatial Decompositions
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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e-differential privacy (e-DP) is a strong and rigorous scheme for protecting individuals' privacy while releasing useful statistical information. The main idea is to inject random noise into the results of statistical queries, such that the existence of any single record has negligible impact on the distributions of query results. The accuracy of such randomized results depends heavily upon the query processing technique, which has been an active research topic in recent years. So far, most existing methods focus on 1-dimensional queries. The only work that handles multi-dimensional query processing under e-DP is [1], which indexes the sensitive data using variants of the quad-tree and the k-d-tree. As we point out in this paper, these structures are inherently suboptimal for answering queries under e-DP. Consequently, the solutions in [1] suffer from several serious drawbacks, including limited and unstable query accuracy, as well as bias towards certain types of queries. Motivated by this, we propose the DP-tree, a novel index structure for multi-dimensional query processing under e-DP that eliminates the problems encountered by the methods in [1]. Further, we show that the effectiveness of the DP-tree can be improved using statistical information about the query workload. Extensive experiments using real and synthetic datasets confirm that the DP-tree achieves significantly higher query accuracy than existing methods. Interestingly, an adaptation of the DP-tree also outperforms previous 1D solutions in their restricted scope, by large margins.