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Authors: Feten Ben Fredj 1 ; Nadira Lammari 2 and Isabelle Comyn-Wattiau 3

Affiliations: 1 CEDRIC-CNAM and Pôle technologique de Sfax, France ; 2 CEDRIC-CNAM, France ; 3 CEDRIC-CNAM and ESSEC Business School, France

Keyword(s): Anonymization, Privacy, Generalization Technique, K-Anonymity, Algorithm, Guidelines.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Best Practices & Communities of Practice ; Communities of Practice ; Computer-Supported Education ; Information Security ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Learning/Teaching Methodologies and Assessment ; Society, e-Business and e-Government ; Symbolic Systems ; Web Information Systems and Technologies

Abstract: Many techniques, such as generalization algorithms have been proposed to ensure data anonymization before publishing. However, data publishers may feel unable to choose the best algorithm given their specific context. In this position paper, we describe synthetically the main generalization algorithms focusing on their constraints and their advantages. Then we discuss the main criteria that can be used to choose the best algorithm given a context. Two use cases are proposed, illustrating guidelines to help data holders choosing an algorithm. Thus we contribute to knowledge management in the field of anonymization algorithms. The approach can be applied to select an algorithm among other anonymization techniques (micro-aggregation, swapping, etc.) and even first to select a technique.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ben Fredj, F.; Lammari, N. and Comyn-Wattiau, I. (2014). Characterizing Generalization Algorithms - First Guidelines for Data Publishers. In Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2014) - KMIS; ISBN 978-989-758-050-5; ISSN 2184-3228, SciTePress, pages 360-366. DOI: 10.5220/0005154603600366

@conference{kmis14,
author={Feten {Ben Fredj}. and Nadira Lammari. and Isabelle Comyn{-}Wattiau.},
title={Characterizing Generalization Algorithms - First Guidelines for Data Publishers},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2014) - KMIS},
year={2014},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005154603600366},
isbn={978-989-758-050-5},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2014) - KMIS
TI - Characterizing Generalization Algorithms - First Guidelines for Data Publishers
SN - 978-989-758-050-5
IS - 2184-3228
AU - Ben Fredj, F.
AU - Lammari, N.
AU - Comyn-Wattiau, I.
PY - 2014
SP - 360
EP - 366
DO - 10.5220/0005154603600366
PB - SciTePress