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Data-sharing

Data Pipeline Challenges of 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 …

Data protection in a borderless digital landscape

A group of the attendees from the PETs workshop

Privacy Enhancing Technologies (PETs) have become an increasingly important policy priority for governments, multilateral organisations, and the data privacy expert community. PETs refer to a range of digital technologies and techniques that enable the collection, processing, analysis, and sharing of …

Guidance for Local Authorities on Data Analytics in Children’s Social Care 

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Children’s Social Care is one of the most important functions the government carries out. Children who need help and protection deserve high quality and effective support as soon as a need is identified.   Data analytics tools, software that enables categorisation …

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

Posted by: and , Posted on: - Categories: Data, Data-sharing, Responsible innovation

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 …