Ravi Kumar
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We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth of the query family’s incidence graph. Our algorithms attain optimal error rates across all regimes and are realized by two different approaches: the first is based on linear programming (LP) and the FPT of the separation problem for the LP dual; the second is based on a subsampled private multiplicative weights method, where we obtain FPT for sampling from Gibbs distributions. Both approaches are unified by a dynamic programming framework over a tree decomposition.
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In this work, we prove computational lower bounds against differentially private (DP) coreset. Specifically, assuming the existence of one-way functions, we show that no polynomial-time $(\epsilon, 1/n^{\omega(1)})$-DP algorithm can compute $(\alpha, \beta)$-coreset for $k$-means in the $\ell_\infty$ metric for some constant $\alpha > 1$. For the Euclidean metric, we show a similar result but only for $\alpha = 1 + \Theta\left(\frac{1}{d^2}\right)$ where $d$ is the dimension.
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VaultGemma
Lynn Chua
Prem Eruvbetine
Chiyuan Zhang
Thomas Mesnard
Borja De Balle Pigem
Daogao Liu
Amer Sinha
Pritish Kamath
Yangsibo Huang
Christopher A. Choquette-Choo
George Kaissis
Armand Joulin
Andreas Terzis
Da Yu
Ryan McKenna
Ruibo Liu
arxiv (2025)
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In this work, we present VaultGemma 1B, a model based on the Gemma family of models fully trained with differential privacy. VaultGemma 1B is 1 billion parameter pretrained model based on the Gemma 2 series of models and uses the same dataset for training. We will be releasing a tech report and the weights of this model.
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User-level differentially private stochastic convex optimization (DP-SCO) has garnered significant attention due to the paramount importance in safeguarding user privacy in large-scale machine learning applications. Current methods, such as those based on Differentially Private Stochastic Gradient Descent (DP-SGD), often struggle with high noise accumulation and suboptimal utility due to the need to privatize every intermediate iterate. In this work, we introduce a novel linear-time algorithm that leverages robust statistics, specifically the geometric median and trimmed mean, to overcome these challenges. Our approach uniquely bounds the sensitivity of all intermediate iterates of SGD with gradient estimation based on robust statistics, thereby significantly reducing the gradient estimation noise and enhancing the privacy-utility trade-off. By sidestepping the repeated privatization required by previous methods, our algorithm not only achieves an improved theoretical privacy-utility balance but also maintains computational efficiency. This work sets the stage for more robust and efficient privacy-preserving techniques in machine learning, with implications for future research and application in the field.
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On the Differential Privacy and Interactivity of Privacy Sandbox Reports
Charlie Harrison
Pritish Kamath
Alexander Knop
Ethan Leeman
Vikas Sahu
PETS (2025)
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The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities as part of the effort to limit third-party cookies. In particular, the Private Aggregation API (PAA) and the Attribution Reporting API (ARA) can be used for ad measurement while providing different guardrails for safeguarding user privacy, including a framework for satisfying differential privacy (DP). In this work, we provide an abstract model for analyzing the privacy of these APIs and show that they satisfy a formal DP guarantee under certain assumptions. Our analysis handles the case where both the queries and database can change interactively based on previous responses from the API.
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How Unique is Whose Web Browser? The role of demographics in browser fingerprinting
Pritish Kamath
Robin Lassonde
Proceedings on Privacy Enhancing Technologies 2025(1) (2025)
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Web browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique "fingerprints". This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies did not openly publish their data. Additionally, data in prior studies had biases and lacked user demographics.
Here we publish a first-of-its-kind open dataset that includes browser attributes with users' demographics, collected from 8,400 US study participants, with their informed consent. Our data collection process also conducted an experiment to study what impacts users' likelihood to share browser data for open research, in order to inform future data collection efforts, with survey responses from a total of 12,461 participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect.
In addition we demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users' age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level, ethnicity and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk.
Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we openly publish can be used to further study research questions not addressed in this work.
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PREM: Privately Answering Statistical Queries with Relative Error
Sushant Sachdeva
Cristóbal Guzmán
Alexander Knop
Pritish Kamath
COLT (2025)
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We introduce PREM (Private Relative Error Multiplicative weight update) a new (ε, δ)-DP mechanism for privately generating synthetic data that achieves a relative error guarantee for linear queries. Namely, for a domain X, a family F of queries f : X → {0, 1}, and ζ > 0, the mechanism on input dataset D ∈ X^n outputs a synthetic dataset D ̃ ∈ X^n such that all linear queries in F on D, namely sum_x∈D f(x) for f ∈ F, are within a 1 ± ζ multiplicative factor of the corresponding value on D ̃ up to an additive error that is polynomial in log |F|, log |X |, log n, log(1/δ), 1/ε, and 1/ζ. This is in contrast to the standard additive-error only setting considered in the literature, which is known to require polynomial error in at least one of n, |F|, or |X |. We complement the algorithm with nearly matching lower bounds.
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Linear Elastic Caching via Ski Rental
Todd Lipcon
The biennial Conference on Innovative Data Systems Research (2025)
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In this work we study the Linear Elastic Caching problem, where the goal is to minimize the total cost of a cache inclusive of not just its misses, but also its memory footprint integrated over time. We demonstrate a theoretical connection to the classic ski rental problem and propose a practical algorithm that combines online caching algorithms with ski rental policies. We also introduce a lightweight machine learning-based algorithm for ski rental that is optimized for production workloads and is easy to integrate within existing database systems. Evaluations on both production workloads in Google Spanner and publicly available traces show that the proposed elastic caching approach can significantly reduce the total cache cost compared to traditional fixed-size cache policies.
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Scaling Embedding Layers in Language Models
Da Yu
Yangsibo Huang
Pritish Kamath
Daogao Liu
Chiyuan Zhang
2025
Quantifying Cross-Modality Memorization in Vision-Language Models
Chiyuan Zhang
Tom Goldstein
Yuxin Wen
Yangsibo Huang
Advances in Neural Information Processing Systems (2025)
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Understanding what and how neural networks memorize during training is crucial, both from the perspective of unintentional memorization of potentially sensitive information and from the standpoint of effective knowledge acquisition for real-world, knowledge-intensive tasks. While previous studies primarily investigate memorization within a single modality, such as text memorization in large language models or image memorization in diffusion models, unified multimodal models are becoming increasingly prevalent in practical applications. In this work, we focus on the unique characteristics of cross-modality memorization and conduct a systematic study centered on vision-language models. To facilitate controlled experiments, we first introduce a synthetic persona dataset comprising diverse synthetic person images and textual descriptions. We quantify factual knowledge memorization and cross-modal transferability by training models on a single modality and evaluating their performance in the other. Our results reveal that facts learned in one modality transfer to the other, but a significant gap exists between recalling information in the source and target modalities. Furthermore, we observe that this gap exists across various scenarios, including more capable models, machine unlearning, and the multi-hop case. At the end, we propose a baseline method to mitigate this challenge. We hope our study can inspire future research on developing more robust multimodal learning techniques to enhance cross-modal transferability.
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