Authors:
Ozgu Can
and
Buket Usenmez
Affiliation:
Department of Computer Engineering, Ege University, 35100 Bornova-Izmir and Turkey
Keyword(s):
Data Anonymization, Data Privacy, Data Security, Knowledge Engineering, Ontology, Semantic Web.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
Various organizations share sensitive personal data for data analysis. Therefore, sensitive information must be protected. For this purpose, privacy preservation has become a major issue along with the data disclosure in data publishing. Hence, an individual’s sensitive data must be indistinguishable after the data publishing. Data anonymization techniques perform various operations on data before it’s shared publicly. Also, data must be available for accurate data analysis when data is released. Therefore, differential privacy method which adds noise to query results is used. The purpose of data anonymization is to ensure that data cannot be misused even if data are stolen and to enhance the privacy of individuals. In this paper, an ontology-based approach is proposed to support privacy-preservation methods by integrating data anonymization techniques in order to develop a generic anonymization model. The proposed personalized privacy approach also considers individuals’ different p
rivacy concerns and includes privacy preserving algorithms’ concepts.
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