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Systematic evaluation of probabilistic K-anonymity for privacy preserving micro-data publishing and analysis
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-2564-0683
Department of Computer Science, University of Umeå, Sweden.ORCID iD: 0000-0002-0368-8037
2021 (English)In: Proceedings of the 18th International Conference on Security and Cryptography, SECRYPT 2021 / [ed] Sabrina De Capitani di Vimercati; Pierangela Samarati, SciTePress, 2021, p. 307-320Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
SciTePress, 2021. p. 307-320
Series
International Joint Conference on e-Business and Telecommunications - SECRYPT, ISSN 2184-7711
Keywords [en]
Anonymization, Data Privacy, Privacy Preserving Machine Learning, Statistical Disclosure Control, Cryptography, Information analysis, Data anonymization, Data publishing, Disclosure risk, Empirical evaluations, Privacy models, Privacy preserving, Privacy-preserving data publications, Systematic evaluation, Privacy by design
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-20485DOI: 10.5220/0010560703070320ISI: 000720102500025Scopus ID: 2-s2.0-85111886138ISBN: 978-989-758-524-1 (print)OAI: oai:DiVA.org:his-20485DiVA, id: diva2:1586103
Conference
18th International Conference on Security and Cryptography, SECRYPT 2021, Virtual, Online, 6 July 2021 - 8 July 2021
Part of project
Disclosure risk and transparency in big data privacy, Swedish Research Council
Funder
Swedish Research Council, 2016-03346
Note

CC BY-NC-ND 4.0

Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

This work is supported by Vetenskapsrådet project:”Disclosure risk and transparency in big data privacy” (VR 2016-03346, 2017-2020)

DRIAT

Available from: 2021-08-19 Created: 2021-08-19 Last updated: 2022-01-26Bibliographically approved

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Other links

Publisher's full textScopushttps://dblp.org/db/conf/secrypt/secrypt2021.html

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Senavirathne, NavodaTorra, Vicenç

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