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• W2100773064 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." @default.
• W2100773064 created "2016-06-24" @default.
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• W2100773064 date "2013-04-01" @default.
• W2100773064 modified "2023-09-25" @default.
• W2100773064 title "Differential-Private Data Publishing Through Component Analysis." @default.
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