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

Li H, Xiong L, Zhang L, Jiang X - Proceedings VLDB Endowment (2014)

Bottom Line: DPCopula computes a differentially private copula function from which synthetic data can be sampled.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.

View Article: PubMed Central - PubMed

Affiliation: Math and Computer Science Department, Emory University, Atlanta, GA.

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 privacy 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 data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error 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 random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. 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.

No MeSH data available.


Related in: MedlinePlus

Synthetic data generation
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Figure 2: Synthetic data generation

Mentions: 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 privacy 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 data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. The main approaches of existing work can be illustrated by Figure 2(a) and classified into two categories: 1) parametric methods that fit the original data to a multivariate distribution and makes inferences about the parameters of the distribution (e.g. [9]). 2) non-parametric methods that learn empirical distributions from the data through histograms (e.g. [7, 12, 3, 4]). Most of these work well for single dimensional or low-order data, but become problematic for data with high dimensions and large attribute domains. This is due to the facts that:


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

Li H, Xiong L, Zhang L, Jiang X - Proceedings VLDB Endowment (2014)

Synthetic data generation
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496798&req=5

Figure 2: Synthetic data generation
Mentions: 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 privacy 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 data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. The main approaches of existing work can be illustrated by Figure 2(a) and classified into two categories: 1) parametric methods that fit the original data to a multivariate distribution and makes inferences about the parameters of the distribution (e.g. [9]). 2) non-parametric methods that learn empirical distributions from the data through histograms (e.g. [7, 12, 3, 4]). Most of these work well for single dimensional or low-order data, but become problematic for data with high dimensions and large attribute domains. This is due to the facts that:

Bottom Line: DPCopula computes a differentially private copula function from which synthetic data can be sampled.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.

View Article: PubMed Central - PubMed

Affiliation: Math and Computer Science Department, Emory University, Atlanta, GA.

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 privacy 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 data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error 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 random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. 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.

No MeSH data available.


Related in: MedlinePlus