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Authors: Fatemeh Amiri 1 ; Gerald Quirchmayr 1 and Peter Kieseberg 2

Affiliations: 1 University of Vienna, Vienna, Department of Computer Science, Austria, SBA Research Institue, Vienna and Austria ; 2 St. Poelten University of Applied Sciences, St. Poelten and Austria

ISBN: 978-989-758-319-3

Keyword(s): Privacy-preserving, E-business, Big Data, Data Mining, Machine Learning.

Related Ontology Subjects/Areas/Topics: Data and Application Security and Privacy ; Data Protection ; Database Security and Privacy ; Information and Systems Security ; Information Assurance ; Information Hiding ; Network Security ; Privacy ; Privacy Enhancing Technologies ; Security and Privacy for Big Data ; Security in Information Systems ; Wireless Network Security

Abstract: This paper aims at identifying and presenting useful solutions to close the privacy gaps in some definite data mining tasks with three primary goals. The overarching aim is to keep efficiency and accuracy of data mining tasks that handle the operations while trying to improve privacy. Specifically, we demonstrate that a machine learning methodology is an appropriate choice to preserve privacy in big data. As core contribution we propose a model consisting of several representative efficient methods for privacy-preserving computations that can be used to support data mining. The planned outcomes and contributions of this paper will be a set of improved methods for privacy-preserving soft-computing based clustering in distributed environments for e-business applications. The proposed model demonstrates that soft computing methods can lead to novel results not only to promote the privacy protection, but also for retaining performance and accuracy of regular operations, especially in onli ne business applications. (More)

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Paper citation in several formats:
Amiri, F.; Quirchmayr, G. and Kieseberg, P. (2018). A Machine Learning Approach for Privacy-preservation in E-business Applications.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT, ISBN 978-989-758-319-3, pages 443-452. DOI: 10.5220/0006826306090618

@conference{secrypt18,
author={Fatemeh Amiri. and Gerald Quirchmayr. and Peter Kieseberg.},
title={A Machine Learning Approach for Privacy-preservation in E-business Applications},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,},
year={2018},
pages={443-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006826306090618},
isbn={978-989-758-319-3},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,
TI - A Machine Learning Approach for Privacy-preservation in E-business Applications
SN - 978-989-758-319-3
AU - Amiri, F.
AU - Quirchmayr, G.
AU - Kieseberg, P.
PY - 2018
SP - 443
EP - 452
DO - 10.5220/0006826306090618

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