Preventing Interval-based Inference by Random Data Perturbation

Publication Type

Conference Proceeding Article

Publication Date

4-2002

Abstract

Random data perturbation (RDP) method is often used in statistical databases to prevent inference of sensitive information about individuals from legitimate sum queries. In this paper, we study the RDP method for preventing an important type of inference: interval-based inference. In terms of interval-based inference, the sensitive information about individuals is said to be compromised if an accurate enough interval, called inference interval, is obtained into which the value of the sensitive information must fall. We show that the RDP methods proposed in the literature are not effective for preventing such interval-based inference. Based on a new type of random distribution, called Ɛ-Gaussian distribution, we propose a new RDP method to guarantee no interval-based inference.

Discipline

Information Security

Research Areas

Information Security and Trust

Publication

Privacy enhancing technologies: 2nd International Workshop, PET 2002, San Francisco, CA, April 14 - 15: Revised Papers

Volume

2482

First Page

160

Last Page

170

ISBN

9783540364672

Identifier

10.1007/3-540-36467-6_12

Publisher

Springer Verlag

City or Country

San Francisco, CA

Additional URL

http://dx.doi.org/10.1007/1-4020-8128-6_6

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