Is Entropy enough for measuring Privacy?

Sevgi Arca, Rattikorn Hewett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Anonymization is critical to privacy. It helps protect the identity and sensitive information of individuals from their profile data. Knowing the degree of anonymity attained is an important step to advance privacy and anonymization techniques. However, little research has focused on articulating a measure to quantify the quality of anonymization. On the other hand, many have used popular Shannon's entropy, a well-established measure from information theory, as a way to measure anonymity. In this paper, we take a closer look at the meaning, the distinction and the relationship between anonymity and entropy with respect to privacy. We argue that, even though information entropy is used amply as a metric for anonymity, it is not a befitting measure. Furthermore, although parts of the entropy's information theory are relevant, they alone are not adequate to be a proper measure for anonymity. This paper presents a simple, intuitive, and theoretically grounded measure for anonymity. We provide a comparison analysis between our measure with other entropy-based metrics along with experiments to show the effectiveness of our proposed measure.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1335-1340
Number of pages6
ISBN (Electronic)9781728176246
DOIs
StatePublished - Dec 2020
Event2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States
Duration: Dec 16 2020Dec 18 2020

Publication series

NameProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conference

Conference2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/TerritoryUnited States
CityLas Vegas
Period12/16/2012/18/20

Keywords

  • anonymity measure
  • information entropy
  • privacy Late Breaking Paper - CSCI-ISCS

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