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Engineering risk-based anonymisation solutions for complex data environments


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Technological advancements have dramatically increased the ability to collect, store and process vast quantities of data. The general applicability and precision of analytical tools in artificial intelligence and machine learning have driven organisations to leverage these advances to process personal data in new and innovative ways. As stewards of personal data, organisations need to keep that data safe and ensure processing is legal and appropriate. Having more data, however, has also led to an increased interest to process personal data for purposes other than why they were originally collected, known as secondary purposes. The reuse of personal data introduces important regulatory challenges, increasing the need to disassociate data used for secondary purposes from personal data, be it to safeguard the data, support a legitimate interest, or anonymise the data. Whereas some academics have focused on specific issues preventing more widespread adoption of this privacy-enhancing technology, others have reframed the discussion around anonymisation as risk management. Combining technology-enabled processes with measures of identifiability provides an opportunity to meet complex business needs while ensuring best practice is adopted in reusing sensitive data. This paper examines these many considerations and demonstrates how risk-based anonymisation can and should be detailed, evidence based and objectively supported through measures of identifiability. The engineering of privacy solutions, through the application of risk-based anonymisation, is also briefly explored for complex use cases involving data lakes and hub and spoke data collection, to provide the reader with a deeper understanding of real-world riskbased anonymisation in practice.
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Keywords: anonymisation; de-identification; privacy engineering; pseudonymisation; secondary uses

Document Type: Research Article

Affiliations: 1: Chief Methodologist, Privacy Analytics 2: Senior Data Scientist, Privacy Analytics

Publication date: June 1, 2020

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  • Journal of Data Protection & Privacy publishes in-depth, peer-reviewed articles, case studies and applied research on all aspects of data protection, information security and privacy issues across the European Union and other jurisdictions, in the wake of the new EU General Data Protection Regulation (GDPR) and the biggest change in data protection and privacy for two decades.
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