Privacy Enhancing Technologies (PET)

Privacy Enhancing Technologies (PETs) are digital solutions that allow information to be collected, processed, analysed, and shared while protecting data confidentiality and privacy.

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European Data Protection Supervisor

The purpose of this page is to provide an overview of Federated Learning as a method for developing machine-learning models.

S. Rossello, R. Díaz Morales, L. Muñoz-González (2021)

This article investigates some of the data protection implications of an emerging privacy preserving machine learning technique, i.e. federated machine learning. First, it shortly describes how this technique works and focuses on some of the main security threats it faces. Second, it presents some of the ways in which this technique can facilitate compliance with certain principles of the General Data Protection Regulation as well as some of the challenges it may pose under the latter.

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The paper discusses the potential value of combining data from various sources for analysis in sectors like healthcare, public administration, and finance, despite challenges related to data sharing due to privacy legislation. It highlights Privacy-Enhancing Technologies (PETs) as a solution, enabling robust analysis of combined data while maintaining privacy. The report proposes integrating PETs into Data Spaces, leveraging research concepts to demonstrate how collaboration among parties can facilitate effective PET use beyond technological considerations, thus shaping future R&D agendas.

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Data Spaces and Privacy Enhancing Technologies (PETs) have a common goal: making insights from data accessible in a confidential manner. But despite this overlap, the development of data spaces and PETs are driven by two different communities. According to Freek Bomhof and Harrie Bastiaansen, both consultants at TNO and affiliated with the CoE-DSC, this must change. Both Freek and Harrie were involved in the development of a joint Big Data Value Association (BDVA) and CoE-DSC whitepaper ‘Leveraging the benefits of combining data spaces and privacy enhancing technologies’. We spoke with them about why applying PETs within data spaces, confidentially exchanging insights from (privacy sensitive) data becomes more scalable.

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In dit whitepaper heeft het IP&T-team van Pels Rijcken – tezamen met technische experts van Linksight en TNO – deze juridische aspecten (in relatie tot de technische aspecten) van de inzet van MPC verkend. Gezien de grote hoeveelheid aan verschijningsvormen die MPC kent, is ervoor gekozen om twee specifieke decentrale MPC-toepassingen tot uitgangspunt te nemen in dit whitepaper, namelijk een specifieke vorm van ‘homomorfe encryptie’ (in dit whitepaper ook wel “decentrale homomorfe encryptie”) en een specifieke vorm van ‘secret sharing’, namelijk een die is gebaseerd op een ‘full threshold secret sharing scheme’ (in dit whitepaper ook wel aangeduid als “secret sharing”).