Differential-private data publishing through component analysis

作者：Jiang Xiaoqian; Ji Zhanglong; Wang Shuang; Mohammed Noman; Cheng Samuel; Ohno Machado Lucila

来源：Transactions on Data Privacy, 2013, 6(1): 19-34.

• Component analysis
• Data dissemination
• Data publishing
• Differential privacies
• Laplacians
• Linear discriminant analysis
• Privacy Preserving Data Publishing
• State-of-the-art methods

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.

• 出版日期2013
• 单位The University Of Oklahoma; UNIVERSITY OF OKLAHOMA; The University of Oklahoma