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Articles by David Darais
Guidelines for Evaluating Differential Privacy Guarantees
Joseph Near, David Darais, This publication describes differential-privacy -- a mathematical framework that quantifies privacy loss to entities when their data appears in a dataset. It serves to fulfill one of the assignments to the National Institute of Standards and Technology (NIST) by the Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence issued on October 30, 2023.
Cybersecurity Insights
This blog is part of a larger NIST series during the month of October for Cybersecurity Awareness Month , called 'Staff Stories Spotlight.' Throughout the month of October this year, Q&A style blogs This blog is part of a larger NIST series during the month of October for Cybersecurity Awareness Month , called 'Staff Stories Spotlight.' Throughout the month of October this year, Q&A style blogs Joseph Near, David Darais and Mark Durkee This post is part of a series on privacy-preserving...
The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction
By: Joseph Near, David Darais, Naomi Lefkovitz and Dave Buckley 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 and AI, fueled by large-scale data availability and high-performance computing, have had a significant impact across the world in the past two decades.
How to deploy machine learning with differential privacy
Credit: metamorworks/shutterstock.com We are delighted to introduce the final guest authors in our blog series, Nicolas Papernot and Abhradeep Thakurta, research scientists at Google Brain, whose research explores applications of differential privacy to machine learning. - Joseph Near and David Darais Previous posts in this series have explored differential privacy for traditional data analytics tasks, such as aggregate queries over database tables.
Differential Privacy Bugs and Why They’re Hard to Find
Operations Differential Privacy Bugs and Why They’re Hard to Find Differentially private programs provide randomized outputs, and privacy bugs aren’t detectable by observing output values Published: Tuesday, June 15, 2021 - 12:02 All articles in this series: In previous articles we have explored what differential privacy is, how it works, and how to answer questions about data in ways that protect privacy.
Summation and Average Queries: Detecting Trends in Your Data
Risk Management Summation and Average Queries: Detecting Trends in Your Data The importance of upper limits and clipping Published: Tuesday, May 11, 2021 - 12:03 All articles in this series: In our last article, we discussed how to determine how many people drink pumpkin spice lattes in a given time period without learning their identifying information.
Counting Queries: Extracting Key Business Metrics From Datasets
Risk Management Counting Queries: Extracting Key Business Metrics From Datasets How to achieve differential privacy with common counting queries Published: Wednesday, April 21, 2021 - 12:03 How many people drink pumpkin spice lattes in October, and how would you calculate this without learning specifically who is drinking them, and who is not? Although they seem simple or trivial, counting queries are used extremely often.
Threat Models for Differential Privacy
Risk Management Threat Models for Differential Privacy A look at central, local, and hybrid models Published: Monday, April 12, 2021 - 12:03 It’s not so simple to deploy a practical system that satisfies differential privacy. Our example in the last post was a simple Python program that adds Laplace noise to a function computed over the sensitive data. For this to work in practice, we’d need to collect all the sensitive data on one server to run our program. What if that server gets hacked?
Differential Privacy for Privacy-Preserving Data Analysis: An Introduction to our Blog Series
Does your organization want to aggregate and analyze data to learn trends, but in a way that protects privacy? Or perhaps you are already using differential privacy tools, but want to expand (or share) your knowledge? In either case, this blog series is for you. Why are we doing this series? Last year, NIST launched a Privacy Engineering Collaboration Space to aggregate open source tools, solutions, and processes that support privacy engineering and risk management.
Summation and Average Queries: Detecting Trends in Your Data
This post is part of a series on differential privacy. Learn more and browse all the posts published to date on the differential privacy blog series page in NIST’s Privacy Engineering Collaboration Space. In our last post, we discussed how to determine how many people drink pumpkin spice lattes in a given time period without learning their identifying information.
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