Date: 2022
Type: Thesis
Towards a taxonomy of technical privacy metrics for the data protection impact assessment
Florence : European University Institute, 2022, EUI, STG, Master Thesis, 2022
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, 2022 - https://hdl.handle.net/1814/74798
Retrieved from Cadmus, EUI Research Repository
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.
Additional information:
Award date: 17 June 2022. Supervisor: Professor Andrea Renda, European University Institute
Cadmus permanent link: https://hdl.handle.net/1814/74798
Full-text via DOI: 10.2870/7871660
Series/Number: EUI; STG; Master Thesis; 2022
Publisher: European University Institute