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A Data Distortion by Probability Distribution.

Chong K. Liew, Uinam J. Choi, Chung J. Liew: A Data Distortion by Probability Distribution. ACM Trans. Database Syst. 10(3): 395-411(1985)
@article{DBLP:journals/tods/LiewCL85,
  author    = {Chong K. Liew and
               Uinam J. Choi and
               Chung J. Liew},
  title     = {A Data Distortion by Probability Distribution},
  journal   = {ACM Trans. Database Syst.},
  volume    = {10},
  number    = {3},
  year      = {1985},
  pages     = {395-411},
  ee        = {http://doi.acm.org/10.1145/3979.4017, db/journals/tods/LiewCL85.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

This paper introduces data distortion by probability distribution, a probability distortion that involves three steps. The first step is to identify the underlying density function of the original series and to estimate the parameters of this density function. The second step is to generate a series of data from the estimated density function. And the final step is to map and replace the generated series for the original one. Because it is replaced by the distorted data set, probability distortion guards the privacy of an individual belonging to the original data set. At the same time, the probability distorted series provides asymptotically the same statistical properties as those of the original series, since both are under the same distribution. Unlike conventional point distortion, probability distortion is difficult to compromise by repeated queries, and provides a maximum exposure for statistical analysis.

Copyright © 1985 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

Joint ACM SIGMOD / IEEE Computer Society Anthology

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References

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Referenced by

  1. Rakesh Agrawal, Ramakrishnan Srikant: Privacy-Preserving Data Mining. SIGMOD Conference 2000: 439-450
  2. Jon M. Kleinberg, Christos H. Papadimitriou, Prabhakar Raghavan: Auditing Boolean Attributes. PODS 2000: 86-91
  3. Daniel Stamate, Henri Luchian, Ben Paechter: A General Model for the Answer-Perturbation Techniques. SSDBM 1994: 90-96
  4. Francesco M. Malvestuto: Statistical versus Relational Join Dependencies. SSDBM 1994: 64-73
  5. Nabil R. Adam, John C. Wortmann: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Comput. Surv. 21(4): 515-556(1989)
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