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How can we explore the unknown properties of high-dimensional sensitiverelational data while preserving privacy? We study how to construct anexplorable privacy-preserving materialized view under differential privacy. Noexisting state-of-the-art methods simultaneously satisfy the followingessential properties in data exploration: workload independence, analyticalreliability (i.e., providing error bound for each search query), applicabilityto high-dimensional data, and space efficiency. To solve the above issues, wepropose HDPView, which creates a differentially private materialized view bywell-designed recursive bisected partitioning on an original data cube, i.e.,count tensor. Our method searches for block partitioning to minimize the errorfor the counting query, in addition to randomizing the convergence, by choosingthe effective cutting points in a differentially private way, resulting in aless noisy and compact view. Furthermore, we ensure formal privacy guaranteeand analytical reliability by providing the error bound for arbitrary countingqueries on the materialized views. HDPView has the following desirableproperties: (a) Workload independence, (b) Analytical reliability, (c) Noiseresistance on high-dimensional data, (d) Space efficiency. To demonstrate theabove properties and the suitability for data exploration, we conduct extensiveexperiments with eight types of range counting queries on eight real datasets.HDPView outperforms the state-of-the-art methods in these evaluations.

HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data