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Evolutionary approach to violating group anonymity using third-party data.

Dan Tavrov1, Oleg Chertov1

  • 1National Technical University of Ukraine "Kyiv Polytechnic Institute", Kyiv, Ukraine.

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|February 5, 2016
PubMed
Summary

Protecting group anonymity in Big Data is challenging. This study introduces an evolutionary computing method to build fuzzy models that can identify group membership, potentially violating privacy even after data anonymization.

Keywords:
Fuzzy inferenceGroup anonymityMemetic algorithmMicrofilePrivacy-preserving data publishingSubgroup discovery

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Area of Science:

  • Data Science
  • Privacy-Preserving Technologies
  • Computational Intelligence

Background:

  • Big Data era poses challenges to restricting access to primary statistical data.
  • Existing methods for individual data anonymity are insufficient to prevent group privacy violations.
  • Removing identifying attributes is a crude solution and does not guarantee complete privacy.

Purpose of the Study:

  • To demonstrate the possibility of violating group anonymity even after removing unique group identifiers.
  • To introduce a novel method for building fuzzy models capable of inferring group membership.
  • To explore data protection strategies against group anonymity violations.

Main Methods:

  • Utilizing third-party data to construct fuzzy models of respondent groups.
  • Employing evolutionary computing to build these fuzzy models.
  • Discussing a memetic approach for data protection against group anonymity breaches.

Main Results:

  • It is possible to violate group anonymity using fuzzy models built from third-party data.
  • Fuzzy rules can determine respondent membership with sufficient certainty to compromise group privacy.
  • An evolutionary computing approach effectively builds these privacy-compromising models.

Conclusions:

  • Group anonymity is vulnerable even when direct identifiers are removed.
  • Fuzzy modeling presents a novel threat to group privacy in Big Data.
  • Developing advanced data protection methods, like memetic approaches, is crucial for safeguarding group anonymity.