Full Citation

Title: Data synthesis via differentially private markov random fields

Citation Type: Journal Article

Publication Year: 2021

ISBN:

ISSN: 2150-8097

DOI: 10.14778/3476249.3476272

NSFID:

PMCID:

PMID:

Abstract: This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data.

Url: https://doi.org/10.14778/3476249.3476272

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Authors: Cai, Kuntai; Lei, Xiaoyu; Wei, Jianxin; Xiao, Xiaokui

Periodical (Full): Proceedings of the VLDB Endowment

Issue: 11

Volume: 14

Pages: 2190-2202

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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