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Trans Data Priv. 2013 Apr;6(1):19-34.

Differential-Private Data Publishing Through Component Analysis.

Author information

1
Division of Biomedical Informatics, UC San Diego, La Jolla, CA 92093.
2
Department of Computer Science, Concordia University, 1455 De Maisonneuve Blvd. W., QA H3G 1M8.
3
University of Oklahoma, 4502 E., 41st St #4403, Tulsa, OK 74135-2512.

Abstract

A reasonable compromise of privacy and utility exists at an "appropriate" resolution of the data. We proposed novel mechanisms to achieve privacy preserving data publishing (PPDP) satisfying ε-differential privacy with improved utility through component analysis. The mechanisms studied in this article are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The differential PCA-based PPDP serves as a general-purpose data dissemination tool that guarantees better utility (i.e., smaller error) compared to Laplacian and Exponential mechanisms using the same "privacy budget". Our second mechanism, the differential LDA-based PPDP, favors data dissemination for classification purposes. Both mechanisms were compared with state-of-the-art methods to show performance differences.

KEYWORDS:

data publishing; differential privacy; linear discriminant analysis; principal component analysis

PMID:
24409205
PMCID:
PMC3883117

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