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The five safes of risk-based anonymization

Arbuckle, Luk; Ritchie, Felix

Authors

Luk Arbuckle



Abstract

The sharing of data for the purposes of data analysis and research can have many benefits. At the same time, concerns and controversies about data ownership and data privacy elicit significant debate. So how do we utilize data in a way that protects individual privacy but still ensures that the data are of sufficient granularity that the analytics will be useful and meaningful? Data anonymization (also called de-identification, depending on the jurisdiction) is the process of removing detail in the data or adding other controls to reduce re-identification risk. Good anonymization should mitigate exposure and allow you to easily demonstrate that you have taken your responsibility toward data subjects seriously.

Journal Article Type Article
Publication Date Sep 1, 2019
Journal IEEE Security and Privacy
Print ISSN 1540-7993
Electronic ISSN 1558-4046
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 17
Issue 5
Pages 84-89
APA6 Citation Arbuckle, L., & Ritchie, F. (2019). The five safes of risk-based anonymization. IEEE Security and Privacy Magazine, 17(5), 84-89. https://doi.org/10.1109/MSEC.2019.2929282
DOI https://doi.org/10.1109/MSEC.2019.2929282
Keywords Computer Networks and Communications; Electrical and Electronic Engineering; Law

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Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.







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