Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Language

English

Abstract

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet, individual privacy has been a major obstacle to data sharing. In this paper, we consider the problem of releasing high-dimensional data with differential privacy guarantees. We propose a novel solution to preserve the joint distribution of a high-dimensional dataset. We first develop a robust sampling-based framework to systematically explore the dependencies among all attributes and subsequently build a dependency graph. This framework is coupled with a generic threshold mechanism to significantly improve accuracy. We then identify a set of marginal tables from the dependency graph to approximate the joint distribution based on the solid inference foundation of the junction tree algorithm while minimizing the resultant error. We prove that selecting the optimal marginals with the goal of minimizing error is NP-hard and, thus, design an approximation algorithm using an integer programming relaxation and the constrained concave-convex procedure. Extensive experiments on real datasets demonstrate that our solution substantially outperforms the state-of-the-art competitors.

Publication Date

8-2015

Source Publication Title

Proceeding KDD 15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Start Page

129

End Page

138

Publisher

ACM

Place of Publication

Sydney, NSW, Australia

DOI

10.1145/2783258.2783379

Link to Publisher's Edition

https://dl.acm.org/citation.cfm?id=2783379

ISBN (print)

9781450336642

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