Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining

TitleGeometric Data Perturbation for Privacy Preserving Outsourced Data Mining
Publication TypeJournal Article
Year of Publication2010
AuthorsKeke Chen, Ling Liu
JournalJournal of Knowledge and Information Systems (KAIS)
KeywordsData mining algorithms, Data perturbation, Geometric data perturbation, Privacy evaluation, Privacy-preserving data mining
Abstract

Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors.We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the GeometricData Perturbation (GDP)method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well.

Full Text

Keke Chen and Ling Liu, ' Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining ', Journal of Knowledge and Information Systems (KAIS), online version, 2010; in print 2011.
research center: Kno.e.sis Research Center
year: 2011
hasURL: http://www.cs.wright.edu/~keke.chen/papers/gdp-kais-online.pdf
hasBookTitle: Journal of Knowledge and Information Systems (KAIS)

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