Fidelity loss in distribution-preserving anonymization and histogram equalization

Lav R Varshney, Kush R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we show a formal equivalence between histogram equalization and distribution-preserving quantization. We use this equivalence to connect histogram equalization to quantization for preserving anonymity under the k-anonymity metric, while maintaining distributional properties for data analytics applications. Finally, we make connections to mismatched quantization. These relationships allow us to characterize the loss in mean-squared error (MSE) performance of privacy-preserving quantizers that must meet distribution-preservation constraints as compared to MSE-optimal quantizers in the high-rate regime. Thus, we obtain a formal characterization of the cost of anonymity.

Original languageEnglish (US)
Title of host publication2016 50th Annual Conference on Information Systems and Sciences, CISS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages24-29
Number of pages6
ISBN (Electronic)9781467394574
DOIs
StatePublished - Apr 26 2016
Event50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States
Duration: Mar 16 2016Mar 18 2016

Publication series

Name2016 50th Annual Conference on Information Systems and Sciences, CISS 2016

Other

Other50th Annual Conference on Information Systems and Sciences, CISS 2016
CountryUnited States
CityPrinceton
Period3/16/163/18/16

Fingerprint

Costs

Keywords

  • Data
  • Histogram equalization
  • K-anonymity
  • Mismatched quantization
  • Neural information processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Varshney, L. R., & Varshney, K. R. (2016). Fidelity loss in distribution-preserving anonymization and histogram equalization. In 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016 (pp. 24-29). [7460471] (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2016.7460471

Fidelity loss in distribution-preserving anonymization and histogram equalization. / Varshney, Lav R; Varshney, Kush R.

2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 24-29 7460471 (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Varshney, LR & Varshney, KR 2016, Fidelity loss in distribution-preserving anonymization and histogram equalization. in 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016., 7460471, 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 24-29, 50th Annual Conference on Information Systems and Sciences, CISS 2016, Princeton, United States, 3/16/16. https://doi.org/10.1109/CISS.2016.7460471
Varshney LR, Varshney KR. Fidelity loss in distribution-preserving anonymization and histogram equalization. In 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 24-29. 7460471. (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016). https://doi.org/10.1109/CISS.2016.7460471
Varshney, Lav R ; Varshney, Kush R. / Fidelity loss in distribution-preserving anonymization and histogram equalization. 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 24-29 (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016).
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