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Data-sharing

Protecting Model Updates in Privacy-Preserving Federated Learning

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In our second post we described attacks on models and the concepts of input privacy and output privacy. ln our previous post, we described horizontal and vertical partitioning of data in privacy-preserving federated learning (PPFL) systems. In this post, we …

Data Distribution in Privacy-Preserving Federated Learning

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This post is part of a series on privacy-preserving federated learning. The series is a collaboration between the Responsible Technology Adoption Unit (RTA) and the US National Institute of Standards and Technology (NIST). Learn more and read all the posts …

Privacy-Preserving Federated Learning: Understanding the Costs and Benefits

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Privacy Enhancing Technologies (PETs) could enable organisations to collaboratively use sensitive data in a privacy-preserving manner and, in doing so, create new opportunities to harness the power of data for research and development of trustworthy innovation. However, research DSIT commissioned …

Privacy Attacks in Federated Learning

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between CDEI and the US National Institute of Standards and Technology (NIST). Learn more and read all the posts published to date on the …

The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction

This post is the first in a series on privacy-preserving federated learning. The series is a collaboration between CDEI and the US National Institute of Standards and Technology (NIST). Advances in machine learning and AI, fuelled by large-scale data availability …

Supporting the adoption of privacy-enhancing technologies

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As set out in the UK government’s National Data Strategy, the CDEI has been exploring the role of privacy-enhancing technologies (PETs) in enabling trustworthy use of data. PETs have the potential to unlock innovation by enabling valuable data sharing and …

Privacy enhancing technologies for trustworthy use of data

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The CDEI has been researching the role of privacy enhancing technologies (PETs) in enabling safe, private and trustworthy use of data. Privacy is a fundamental right. Organisations have an obligation to protect privacy, and must consider important legal, ethical, and …

COVID-19 repository: Local government edition

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The primary purpose of the majority of use-cases has been to support the local response and mitigate the effects of lockdown. However, we are starting to see examples of use-cases designed to build future resilience and aid the recovery; these have been particularly prominent in the transport sector. For example, the Commonplace Mapping Tool which allows users to highlight pinch points across Glasgow City Centre, where measures such as pavement widening and new cycle lanes could be introduced to help people maintain physical distancing.