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Cybersecurity Insights

a NIST blog

Joseph Near

Joseph Near

Joseph Near is an assistant professor of computer science at the University of Vermont who supports NIST as a moderator for the Privacy Engineering Collaboration Space. His research interests include data privacy, computer security, and programming languages. Joseph received his B.S. in computer science from Indiana University, and his M.S. and Ph.D. in computer science from MIT.

Scalability Challenges in Privacy-Preserving Federated Learning

October 8, 2024

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Responsible

Implementation Challenges in Privacy-Preserving Federated Learning

August 20, 2024

In this post, we talk with Dr. Xiaowei Huang and Dr. Yi Dong (University of Liverpool), Dr. Mat Weldon ( United Kingdom (UK) Office of National Statistics (ONS)

Protecting Trained Models in Privacy-Preserving Federated Learning

July 15, 2024

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Responsible

Protecting Model Updates in Privacy-Preserving Federated Learning: Part Two

May 2, 2024

The problem The previous post in our series discussed techniques for providing input privacy in PPFL systems where data is horizontally partitioned. This blog

Protecting Model Updates in Privacy-Preserving Federated Learning

March 21, 2024

In our second post we described attacks on models and the concepts of input privacy and output privacy. ln our last post, we described horizontal and vertical

Data Distribution in Privacy-Preserving Federated Learning

February 27, 2024

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Responsible

Privacy Attacks in Federated Learning

January 24, 2024

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Centre for Data

The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction

December 7, 2023

This post is the first in a series on privacy-preserving federated learning. The series is a collaboration between CDEI and NIST. Advances in machine learning

Differential Privacy: Future Work & Open Challenges

January 24, 2022

In this series of blog posts, we have tried to give an accessible overview of the state-of-the-art in differential privacy. In this final post, we review some

Differential Privacy Bugs and Why They’re Hard to Find

May 25, 2021

In previous posts we have explored what differential privacy is, how it works, and how to answer questions about data in ways that protect privacy. All of the

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