Understanding Hierarchical Methods for Differentially Private Histograms

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Provided by: VLD Digital
Topic: Big Data
Format: PDF
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, the authors examine the factors affecting the accuracy of hierarchical approaches by studying the Mean Squared Error (MSE) when answering range queries. They 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.
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