Data protection considerations for implementing federated learning – a regulator’s perspective
This guest post features reflections from the UK’s Information Commissioner’s Office (ICO).
PETs Blogs
Data Pipeline Challenges of Privacy-Preserving Federated Learning
Posted by:
Joe Near, David Darais and Mark Durkee, Posted on:
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Categories:
Data, Data collection, Data-driven technology, Data-sharing, PETs Blogs
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 …
Scalability Challenges in Privacy-Preserving Federated Learning
Hear from some of the winners of the UK-US PETs Prize Challenges on scalability challenges and solutions in privacy-preserving federated learning.
Implementation Challenges in Privacy-Preserving Federated Learning
Featuring: Dr. Mat Weldon - ONS Dr. Michael Fenton – Trūata Dr. Xiaowei Huang – University of Liverpool Dr. Yi Dong – University of Liverpool In this post, we talk with Dr. Xiaowei Huang and Dr. Yi Dong (University of …
Protecting Trained Models in Privacy-Preserving Federated Learning
Find out how you can protect individual's data after it's been used in training models through output privacy.
Input privacy for vertical federated learning
Posted by:
Dave Buckley, Mark Durkee, Joe Near and David Darais, Posted on:
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Categories:
Data, PETs Blogs
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 …
Protecting Model Updates in Privacy-Preserving Federated Learning
Posted by:
David Darais and Joe Near, Posted on:
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Categories:
Data, Data collection, Data-driven technology, Data-sharing, PETs Blogs
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
Posted by:
Mark Durkee, Dave Buckley, Joe Near and David Darais, Posted on:
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Categories:
Data, Data collection, Data-driven technology, Data-sharing, PETs Blogs
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 Attacks in Federated Learning
Posted by:
Dave Buckley, Mark Durkee, Joe Near and David Darais, Posted on:
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Categories:
Algorithms, Artificial intelligence, Data, Data collection, Data-driven technology, Data-sharing, PETs Blogs
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
Posted by:
Dave Buckley, David Darais, Joe Near and Naomi Lefkovitz, Posted on:
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Categories:
Data, Data collection, Data-driven technology, Data-sharing, Ethical innovation, PETs Blogs, Responsible innovation
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 …
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