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Title: DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing

Citation Type: Miscellaneous

Publication Year: 2014

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Abstract: Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous guarantee. However, to this date there is no open-source tools that allow users to generate differentially private synthetic data, in particular, for high dimensional and large domain data. Most of the existing techniques that generate differentially private histograms or synthetic only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and computation complexity. We propose DPSynthesizer, a toolkit for differentially private data synthesization. The core of DPSynthesizer is DPCopula designed for high-dimensional and large-domain data. DPCopula computes a differentially private copula function from which synthetic data can be sampled. Copula functions are used to describe the dependence between multivariate joint distribution using one-dimensional marginal distribution. DPSynthesizer also implements a set of state-of-the-art methods for building differentially private histograms, suitable for low-dimensional data, from which synthetic data can be generated. We will demonstrate the system using DPCopula as well as other methods with various data sets and show the feasibility, utility, and efficiency of various methods.

Url: http://www.vldb.org/2014/program/papers/demo/p1152-Li.pdf

User Submitted?: No

Authors: Li, Haoran; Xiong, Li; Zhang, Lifan; Jiang, Xiaoqian

Publisher: VLDB

Data Collections: IPUMS USA, IPUMS International

Topics: Methodology and Data Collection

Countries: Brazil