Skip to main content

Data collection

Protecting Model Updates in Privacy-Preserving Federated Learning

Posted by: and , 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: , , and , 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 …

Privacy-Preserving Federated Learning: Understanding the Costs and Benefits

Posted by: and , Posted on: - Categories: Data, Data collection, Data-driven technology, Data-sharing

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 …

Public attitudes on the fair use of data and algorithms in finance

Posted by: , Posted on: - Categories: Algorithms, Bias, Data collection, Decision-making

Financial companies are increasingly using complex algorithms to make decisions regarding loans or insurance - algorithms that look for patterns in data which are associated with risks of default or high insurance claims. This raises risks of bias and discrimination …