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Data-driven technology

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 …

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 …

Driving responsible innovation in self-driving vehicles

Self-driving vehicles have the potential to radically transform the UK’s roads. But to enable their benefits and achieve the government’s ambition to ‘make the UK the best place in the world to deploy connected and automated vehicles’, developers and manufacturers …

Helping recruiters to innovate responsibly with data-driven tools

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The use of data-driven tools is rising across the recruitment sector. The COVID-19 pandemic has heightened the need for effective and efficient digital tools in hiring as recruiters search for the highest calibre candidates in an increasingly virtual world. Companies …