Reconstruction privacy: Enabling statistical learning

• Main Authors: Wang, Ke, HAN, Chao, FU, Ada Waichee, WONG, Raymond C., YU, Philip S.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Data privacy; Differential privacy; Databases and Information Systems; Information Security; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3547; https://ink.library.smu.edu.sg/context/sis_research/article/4548/viewcontent/ReconstructionPrivacy_2015.pdf
• Institution: Singapore Management University

Non-independent reasoning (NIR) allows the information about one record in the data to be learnt from the information of other records in the data. Most posterior/prior based privacy criteria consider NIR as a privacy violation and require to smooth the distribution of published data to avoid sensitive NIR. The drawback of this approach is that it limits the utility of learning statistical relationships. The differential privacy criterion considers NIR as a non-privacy violation, therefore, enables learning statistical relationships, but at the cost of potential disclosures through NIR. A question is whether it is possible to (1) allow learning statistical relationships, yet (2) prevent sensitive NIR about an individual. We present a data perturbation and sampling method to achieve both (1) and (2). The enabling mechanism is a new privacy criterion that distinguishes the two types of NIR in (1) and (2) with the help of the law of large numbers. In particular, the record sampling effectively prevents the sensitive disclosure in (2) while having less effect on the statistical learning in (1).