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Authors: Navoda Senavirathne 1 and Vicenç Torra 2

Affiliations: 1 School of Informatics, University of Skövde, Sweden ; 2 Department of Computer Science, University of Umeå, Sweden

Keyword(s): Data Privacy, Anonymization, Statistical Disclosure Control, Privacy Preserving Machine Learning.

Abstract: In the light of stringent privacy laws, data anonymization not only supports privacy preserving data publication (PPDP) but also improves the flexibility of micro-data analysis. Machine learning (ML) is widely used for personal data analysis in the present day thus, it is paramount to understand how to effectively use data anonymization in the ML context. In this work, we introduce an anonymization framework based on the notion of “probabilistic k-anonymity” that can be applied with respect to mixed datasets while addressing the challenges brought forward by the existing syntactic privacy models in the context of ML. Through systematic empirical evaluation, we show that the proposed approach can effectively limit the disclosure risk in micro-data publishing while maintaining a high utility for the ML models induced from the anonymized data.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Senavirathne, N. and Torra, V. (2021). Systematic Evaluation of Probabilistic k-Anonymity for Privacy Preserving Micro-data Publishing and Analysis. In Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-524-1; ISSN 2184-7711, SciTePress, pages 307-320. DOI: 10.5220/0010560703070320

@conference{secrypt21,
author={Navoda Senavirathne. and Vicen\c{C} Torra.},
title={Systematic Evaluation of Probabilistic k-Anonymity for Privacy Preserving Micro-data Publishing and Analysis},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT},
year={2021},
pages={307-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010560703070320},
isbn={978-989-758-524-1},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT
TI - Systematic Evaluation of Probabilistic k-Anonymity for Privacy Preserving Micro-data Publishing and Analysis
SN - 978-989-758-524-1
IS - 2184-7711
AU - Senavirathne, N.
AU - Torra, V.
PY - 2021
SP - 307
EP - 320
DO - 10.5220/0010560703070320
PB - SciTePress

- Science and Technology Publications, Lda.
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