loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Tomoaki Mimoto ; Anirban Basu and Shinsaku Kiyomoto

Affiliation: KDDI R&D Laboratories Inc, Japan

Keyword(s): Privacy, Anonymization, k-anonymity, Generalization Hierarchy.

Related Ontology Subjects/Areas/Topics: Data and Application Security and Privacy ; Information and Systems Security ; Information Assurance ; Personal Data Protection for Information Systems ; Privacy ; Privacy Enhancing Technologies ; Risk Assessment

Abstract: The privacy of individuals included in the datasets must be preserved when sensitive datasets are published. Anonymization algorithms such as k-anonymization have been proposed in order to reduce the risk of individuals in the dataset being identified. k-anonymization is the most common technique of modifying attribute values in a dataset until at least k identical records are generated. There are many algorithms that can be used to achieve k-anonymity. However, existing algorithms have the problem of information loss due to a tradeoff between data quality and anonymity. In this paper, we propose a novel method of constructing a generalization hierarchy for k anonymization algorithms. Our method analyses the correlation between attributes and generates an optimal hierarchy according to the correlation. The effect of the proposed scheme has been verified using the actual data: the average of k of the datasets is 83:14, and it is around 1=3 of the value obtained by conventional methods.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.226.226.151

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mimoto, T.; Basu, A. and Kiyomoto, S. (2016). Towards Practical k-Anonymization: Correlation-based Construction of Generalization Hierarchy. In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT; ISBN 978-989-758-196-0; ISSN 2184-3236, SciTePress, pages 411-418. DOI: 10.5220/0005963804110418

@conference{secrypt16,
author={Tomoaki Mimoto. and Anirban Basu. and Shinsaku Kiyomoto.},
title={Towards Practical k-Anonymization: Correlation-based Construction of Generalization Hierarchy},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT},
year={2016},
pages={411-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005963804110418},
isbn={978-989-758-196-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - SECRYPT
TI - Towards Practical k-Anonymization: Correlation-based Construction of Generalization Hierarchy
SN - 978-989-758-196-0
IS - 2184-3236
AU - Mimoto, T.
AU - Basu, A.
AU - Kiyomoto, S.
PY - 2016
SP - 411
EP - 418
DO - 10.5220/0005963804110418
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic