Deriving Private Information from Perturbed Data using IQR Based Approach

Publication Type

Conference Proceeding Article

Publication Date

4-2006

Abstract

Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly modifying the data values using some additive noise and aim to reconstruct the original distribution closely at an aggregate level. However, one challenge here is whether the reconstructed distribution can be exploited by attackers or snoopers to derive sensitive individual data. This paper presents one simple attack using Inter-Quantile Range on reconstructed distribution. The experimental results show that current random perturbation-based privacy preserving data mining techniques may need a careful scrutiny in order to prevent privacy breaches through this model based inference.

Discipline

Information Security

Research Areas

Information Security and Trust

Publication

ICDE '06: Proceedings: 22nd International Conference on Data Engineering Workshops, 3-7 April, 2006, Atlanta, Georgia

First Page

92

ISBN

9780769525716

Identifier

10.1109/ICDEW.2006.47

Publisher

IEEE

City or Country

Atlanta, GA

Additional URL

http://dx.doi.org/10.1109/ICDEW.2006.47

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