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).
Data
Data Pipeline Challenges of Privacy-Preserving Federated Learning
Posted by:
Joe Near, David Darais and Mark Durkee, Posted on:
-
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.
Data protection in a borderless digital landscape
Posted by:
Benjamin Moore, Mark Durkee, Henry Ramsay and Christian Reimsbach-Kounatze, Posted on:
-
Categories:
Data, Data collection, Data-driven technology, Data-sharing
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 …
Input privacy for vertical federated learning
Posted by:
Dave Buckley, Mark Durkee, Joe Near and David Darais, Posted on:
-
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 …
Guidance for Local Authorities on Data Analytics in Children’s Social Care
Posted by:
Ben Kelly, Posted on:
-
Categories:
Data, Data collection, Data-sharing, Demographic data
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
Posted by:
David Darais and Joe Near, Posted on:
-
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:
-
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 …
Recent Comments