The five safes of risk-based anonymization

Arbuckle, Luk; Ritchie, Felix

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
• Acceptance Date: Jul 23, 2019
• Online Publication Date: Aug 30, 2019
• Publication Date: Sep 1, 2019
• Deposit Date: Sep 8, 2019
• Publicly Available Date: Sep 9, 2019
• Journal: IEEE Security and Privacy
• Print ISSN: 1540-7993
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 5
• Pages: 84-89
• DOI: https://doi.org/10.1109/MSEC.2019.2929282
• Keywords: Computer Networks and Communications; Electrical and Electronic Engineering; Law
• Public URL: https://uwe-repository.worktribe.com/output/2872585
• Contract Date: Sep 9, 2019