Towards Data Anonymization in Data Mining via Meta-Heuristic Approaches

Towards Data Anonymization in Data Mining via Meta-Heuristic Approaches

Abstract

In this paper, a meta-heuristics model proposed to protect the confidentiality of data through anonymization. The aim is to minimize information loss as well as the maximization of privacy protection using Genetic algorithms and fuzzy sets. As a case study, Kohonen Maps put in practice through Self Organizing Map (SOM) applied to test the validity of the proposed model. SOM suffers from some privacy gaps and also demands a computationally, highly complex task. The experimental results show an improvement of protection of sensitive data without compromising cluster quality and optimality.

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Authors
  • Amiri, Fatemeh
  • Quirchmayr, Gerald
  • Kieseberg, Peter
  • Bertone, Alessio
  • Weippl, Edgar
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
14th International Workshop on Data Privacy Management(DPM), The European Symposium on Research in Computer Security (ESORICS) 2019
Divisions
Multimedia Information Systems
Event Location
Luxemburg
Event Type
Workshop
Event Dates
26 Sept 2019
Series Name
Data Privacy Management, Cryptocurrencies and Blockchain Technology
ISSN/ISBN
0302-9743 / 978-3-030-31499-6
Page Range
pp. 39-48
Date
2019
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