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      Optimizing error of high-dimensional statistical queries under differential privacy

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          Abstract

          Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data, such as statistical agencies and medical institutions, use them to safely release summary tabulations. However, existing techniques are accurate only on a narrow class of query workloads, or are extremely slow, especially when analyzing more than one or two dimensions of the data. In this work we propose HDMM, a new differentially private algorithm for answering a workload of predicate counting queries, that is especially effective for higher-dimensional datasets. HDMM represents query workloads using an implicit matrix representation and exploits this compact representation to efficiently search (a subset of) the space of differentially private algorithms for one that answers the input query workload with high accuracy. We empirically show that HDMM can efficiently answer queries with lower error than state-of-the-art techniques on a variety of low and high dimensional datasets.

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          Most cited references 17

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          Updating the Inverse of a Matrix

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            The ubiquitous Kronecker product

             Charles Loan (2000)
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              Privacy integrated queries

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                Author and article information

                Journal
                10 August 2018
                Article
                10.14778/3231751.3231769
                1808.03537

                http://creativecommons.org/licenses/by-nc-sa/4.0/

                Custom metadata
                PVLDB, 11 (10): 1206-1219, 2018
                cs.DB cs.CR

                Databases, Security & Cryptology

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