Differential-Private Data Publishing Through Component Analysis

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.