An ontology based personalized privacy preservation

Küçük Resim Yok

Tarih

2019

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

SciTePress

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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 privacy concerns and includes privacy preserving algorithms’ concepts. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Açıklama

Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 -- 17 September 2019 through 19 September 2019 -- -- 152660

Anahtar Kelimeler

Data Anonymization, Data Privacy, Data Security, Knowledge Engineering, Ontology, Semantic Web

Kaynak

IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

WoS Q Değeri

Scopus Q Değeri

N/A

Cilt

2

Sayı

Künye