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Authors: Jack R. Davies and Jianhua Shao

Affiliation: School of Computer Science & Informatics, Cardiff University, U.K.

Keyword(s): Data Anonymisation, Data Utility, Decision Tree.

Abstract: Privacy Preserving Data Publishing (PPDP) is a practice for anonymising microdata such that it can be publicly shared. Much work has been carried out on developing methods of data anonymisation, but relatively little work has been done on examining how useful anonymised data is in supporting data analysis. This paper evaluates the utility of k-anonymised data in decision tree derivation and examines how accurate some commonly used metrics are in estimating this utility. Our results suggest that whilst classification accuracy loss is minimal in most common scenarios, using a small selection of simple metrics when calibrating a k-Anonymisation could help significantly improve decision tree classification accuracy for anonymised data.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Davies, J. and Shao, J. (2022). Utility of Anonymised Data in Decision Tree Derivation. In Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-553-1; ISSN 2184-4356, SciTePress, pages 273-280. DOI: 10.5220/0010778300003120

@conference{icissp22,
author={Jack R. Davies. and Jianhua Shao.},
title={Utility of Anonymised Data in Decision Tree Derivation},
booktitle={Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP},
year={2022},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010778300003120},
isbn={978-989-758-553-1},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP
TI - Utility of Anonymised Data in Decision Tree Derivation
SN - 978-989-758-553-1
IS - 2184-4356
AU - Davies, J.
AU - Shao, J.
PY - 2022
SP - 273
EP - 280
DO - 10.5220/0010778300003120
PB - SciTePress