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Preventing Interval-Based Inference by Random Data Perturbation

  • Yingjiu Li
  • Lingyu Wang
  • Sushil Jajodia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2482)

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 ∊-Gaussian distribution, we propose a new RDP method to guarantee no interval-based inference.

Keywords

Statistical Inference Random Noise Tolerance Level Sensitive Information Random Perturbation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yingjiu Li
    • 1
  • Lingyu Wang
    • 1
  • Sushil Jajodia
    • 1
  1. 1.Center for Secure Information SystemsGeorge Mason UniversityFairfaxUSA

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