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Towards a taxonomy of technical privacy metrics for the data protection impact assessment
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Florence : European University Institute, 2022
EUI; STG; Master Thesis
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NGUYEN, Trung Hieu, Towards a taxonomy of technical privacy metrics for the data protection impact assessment, Florence : European University Institute, 2022, EUI, STG, Master Thesis - https://hdl.handle.net/1814/74798
Abstract
Nowadays, most daily activities involve data flows. The data-intensive applications make our lives more convenient and allow a globalized world to connect fast and easily. However, the extensive use of data poses the threat of privacy risks. Numerous privacy breaches have motivated many pieces of legislation on data privacy. The GDPR, as the most comprehensive regulation, formalizes the principle of Privacy by Design (PbD) and requires data controllers to conduct a Data Protection Impact Assessment (DPIA). While PbD advocates for improving data privacy with Privacy Enhancing Technologies (PETs), available frameworks for the DPIA do not consider the privacy measurement of these PETs. This thesis calls for including technical privacy metrics in DPIAs and proposes a taxonomy framework to select the suitable metrics. The taxonomy filters a metric from a series of factors: the measured aspect(s) of privacy, the adversary model, the quality of a metric, and the target audience.
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Award date: 17 June 2022
Supervisor: Prof. Andrea Renda (European University Institute)
Supervisor: Prof. Andrea Renda (European University Institute)