Measure and calibrate operational privacy risks for differentially private algorithms. - interpretable-dp/riskcal
Welcome to this week’s Tip of the Hat! This week’s post is a “back to basics” about de-identification and patron data. Why? After reading a recent article published in the Code4Lib Journal where patron data was not de-identified before combining it with external data sets, now’s a good time as any to remind library workers … Continue reading "So You Want to Work With Patron Data… De-identification Basics"
Measures taken to protect the privacy of visitors
This post first summarizes what browser fingerprinting is, and common defenses. Second, the post presents problems with “dynamic privacy approaches”, and why Brave is skeptical they are effective for protecting against fingerprinting. Third, the post presents Brave’s fingerprinting protections, current, upcoming and longer-term.
Posted by Florian Hartmann, Software Engineer, and Peter Kairouz, Research Scientist, Google Research Federated learning is a distributed way of tr...
Contribute to mikewest/privacy-budget development by creating an account on GitHub.
Posted by Adam Sadilek, Software Engineer and Xerxes Dotiwalla, Product Manager, Google Research (Updated May 18, 2020: We have released a new pa...
Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given ...
Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research Differential privacy (DP) is an approach that enables data anal...
Posted by Fabian Pedregosa and Eleni Triantafillou, Research Scientists, Google Deep learning has recently driven tremendous progress in a wide arr...
Firefox 128 introduces privacy-preserving attribution, allowing advertisers to measure campaign performance while protecting user privacy.
Fabio Barbero's personal website and blog.
Quick reference based on Alison: AI Governance and Ethics and some extra follow-up readings Five Pillars of Responsible AI Bias & Fairness ...
Floral foam 🌼, elephants 🐘 and evil mayors 😈: the useful HyperLogLog algorithm explained as if you were 10 years old!
Below is a collection of interesting articles I’ve read in 2023. Several papers focus on foundations or methodology ranging from mathematical logic and history to causal inference. Two papers look like jokes or hoaxes but are insightful in the end. We consider the history of women in analytic philosophy and the first law and economics program from the early 20th century. There is a primer on synthetic data for policymakers and a short look at working with the pseudoinverse for highly structured matrices. Finally, we survey a scuffle between internet subcultures and consider income inequality globally.
Researcher - Data Scientist
I have a new paper out with my colleagues from UMass Amherst and Penn State: Efficient and Private Marginal Reconstruction with Local Non-Negativity. Marginals are statistics that capture low-dimensional structure and correlations among sets of attributes in a dataset and are an important building block for differentially private algorithms. A marginal can be decomposed into a set of queries called residuals. Our paper studies how to decompose noisy answers to marginals into noisy answers to residuals and how to recombine noisy answers to many residuals into noisy answers to marginals.
Below is a collection of interesting articles I’ve read in 2022. There are two papers in differential privacy: A Better Privacy Analysis of the Exponential Mechanism and Differentially Private Approximate Quantiles. There’s an article in the history of mathematical philosophy and a survey introduction to an area of logic: The introduction of topology into analytic philosophy and Incomplete and Utter Introduction to Modal Logic. Two papers border on the practical side of the philosophy of science: Stylized Facts in the Social Sciences for social science research and Does Academic Research Destroy Stock Return Predictability? for finance.The World Putin Wants discusses Russia’s rhetoric from the war in Ukraine. To round out the collection, we explore the influence of a wayward early 20th century archaeologist, build a simple mathematical model of tennis, ponder whether NLP models have intentional states, and consider the role of private property among early humans.
Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in moder...
Posted by Brendan McMahan and Abhradeep Thakurta, Research Scientists, Google Research In 2017, Google introduced federated learning (FL), an appro...
Posted by Florian Hartmann, Software Engineer, and Peter Kairouz, Research Scientist, Google Research Federated learning is a distributed way of tr...
Below is a collection of interesting articles I’ve read in 2024. Three papers are on differential privacy and adjacent topics. There’s a recent method for differentially private SGD utilizing methods from private query answering, an intuitive watermarking scheme for language models, and a paper from 1986 that proposed $k$-anonymity before it was formalized as a criterion for de-identification. Several papers are on the history of ideas ranging from early twentieth century pragmatism, the synergies and antagonisms between poetry and philosophy, and the relation between periodicals and intellectual progress to the deaths of Analytical Marxism and Effective Altruism.
Below are some interesting books I’ve read in 2025. This year’s list is topically wide-ranging: from the mathematics of data privacy and information theory to the history of economic thought, from early analytic philosophy and contemporary metaphilosophy to weird fiction, and even an early nineteenth-century meditation on the art of eating well. If you have some thoughts on my list or would like to share yours, send me an email at brettcmullins(at)gmail.com. Enjoy the list!
Below are some interesting articles I’ve read in 2025. They fall into a few categories: the mathematics of data privacy, popular mathematics, politics, and nineteenth century philosophy. For data privacy, we look at how technical details may be hindering adoption of differential privacy as well as scaling laws for differentially private language models. Next, we look at excellent popular math articles introducing the Collatz conjecture and the Busy Beaver numbers. In politics, we explore paranoid eighteenth century political rhetoric as well as John Maynard Keynes’ reflections on the Liberal party after a disastrous election in 1924. Finally, we look at two philosophical movements in mid-nineteenth century America and an 1875 article on the relation between the verifiability and discoverability of truths.
I have a new paper out with my colleagues from UMass Amherst and Penn State: Fast Private Adaptive Query Answering for Large Data Domains. Marginals are statistics that capture low-dimensional structure and correlations among sets of attributes in a dataset and are an important building block for differentially private algorithms. In this paper, we focus on answering large workloads of marginals for discrete tabular datasets over large data domains (i.e., many attributes), which is a computational bottleneck for state-of-the-art query answering and synthetic data mechanisms such as AIM. We introduce a new query answering mechanism called AIM+GReM that integrates our GReM-MLE (Gaussian Residual-to-Marginals) reconstruction method with AIM, which yields improved scalability and competitive error on large datasets.
Posted by Mónica Ribero Díaz, Research Scientist, Google Research Differential privacy (DP) is a property of randomized mechanisms that limit the i...
Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given ...