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A deterministic approach for protecting privacy in sensitive personal data

Lookup NU author(s): Dr Demetris AvraamORCiD, Emeritus Professor Paul BurtonORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022, The Author(s). Background: Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. Methods: In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. Results: We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. Conclusions: The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Publication metadata

Author(s): Avraam D, Jones E, Burton P

Publication type: Article

Publication status: Published

Journal: BMC Medical Informatics and Decision Making

Year: 2022

Volume: 22

Issue: 1

Online publication date: 28/01/2022

Acceptance date: 09/01/2022

Date deposited: 23/06/2022

ISSN (electronic): 1472-6947

Publisher: BioMed Central Ltd


DOI: 10.1186/s12911-022-01754-4

PubMed id: 35090447


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Funder referenceFunder name
824989European Commission (Research Directorate General, Research Infrastructures)
MR/N01104X/2Medical Research Council (MRC)
MRC 108439/Z/15/Z