title: Differential-Private Data Publishing Through Component Analysis.
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 privacywith improved utility throughcomponent 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.
authors.affiliation: Division of Biomedical Informatics, UC San Diego, La Jolla, CA 92093.
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Department of Computer Science, Concordia University, 1455 De Maisonneuve Blvd. W., QA H3G 1M8.
authors.forename: Zhanglong
authors.initials: Z
authors.lastname: Ji
authors.affiliation: Division of Biomedical Informatics, UC San Diego, La Jolla, CA 92093.
authors.forename: Shuang
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Computer Science, Concordia University, 1455 De Maisonneuve Blvd. W., QA H3G 1M8.
authors.forename: Noman
authors.initials: N
authors.lastname: Mohammed
authors.affiliation: University of Oklahoma, 4502 E., 41st St #4403, Tulsa, OK 74135-2512.
authors.forename: Samuel
authors.initials: S
authors.lastname: Cheng
authors.affiliation: Division of Biomedical Informatics, UC San Diego, La Jolla, CA 92093.
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
keywords: data publishing; differential privacy; linear discriminant analysis; principal component analysis