Differential-Private Data Publishing Through Component Analysis.

TitleDifferential-Private Data Publishing Through Component Analysis.
Publication TypeJournal Article
Year of Publication2013
AuthorsJiang, X, Ji, Z, Wang, S, Mohammed, N, Cheng, S, Ohno-Machado, L
JournalTrans Data Priv
Volume6
Issue1
Pagination19-34
Date Published2013 Apr
ISSN1888-5063
iDASH CategoryPrivacy Technology
AbstractA 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.
Alternate JournalTrans Data Priv
PubMed ID24409205