期刊
PROCEEDINGS OF THE VLDB ENDOWMENT
卷 6, 期 14, 页码 1954-1965出版社
ASSOC COMPUTING MACHINERY
DOI: 10.14778/2556549.2556576
关键词
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资金
- United States National Science Foundation [1116991]
- United States AFOSR
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1116991] Funding Source: National Science Foundation
In recent years, many approaches to differentially privately publish histograms have been proposed. Several approaches rely on constructing tree structures in order to decrease the error when answer large range queries. In this paper, we examine the factors affecting the accuracy of hierarchical approaches by studying the mean squared error (MSE) when answering range queries. We start with one-dimensional histograms, and analyze how the MSE changes with different branching factors, after employing constrained inference, and with different methods to allocate the privacy budget among hierarchy levels. Our analysis and experimental results show that combining the choice of a good branching factor with constrained inference outperform the current state of the art. Finally, we extend our analysis to multidimensional histograms. We show that the benefits from mploying hierarchical methods beyond a single dimension are significantly diminished, and when there are 3 or more dimensions, it is almost always better to use the Flat method instead of a hierarchy
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