Differential-Private Data Publishing Through Component Analysis
A reasonable compromise of privacy and utility exists at an \"Appropriate\" resolution of the data. The authors proposed novel mechanisms to achieve Privacy Preserving Data Publishing (PPDP) satisfying e-differential privacy with improved utility through component analysis. The mechanisms studied in this paper 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\".