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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- 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 Security and Privacy |
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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