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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Online Differentially Private Clustering: Private to Non-Private Reduction
Vadym Doroshenko
Pritish Kamath
Alexander Knop
Ethan Leeman
Adam Sealfon
NeurIPS (2026)
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We study differentially private k-mean/median clustering in the online streaming setting where a point arrive at each time step and we need to output a set of k centers that optimize the k-mean/median objective of all the points so far. We give a generic transformation that turns the sensitive stream into a differentially private stream, which is a semi-coreset of the original stream and allows us to run any (non-private) online clustering algorithm as a post-processing step. Our algorithm is efficient in terms of errors, space and running time compared to existing algorithms (Epasto et al., 2023; Dupr{\'{e}} la Tour, 2024). Furthermore, our reduction allows us to inherit properties of the non-private clustering, such as consistency (Lattanzi and Vassilvitski, 2017), which was not satisfied by previous DP algorithms.
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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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Differentially Private Insights into AI Use
Daogao Liu
Pritish Kamath
Alexander Knop
Adam Sealfon
Da Yu
Chiyuan Zhang
Conference on Language Modeling (COLM) 2025 (2025)
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We introduce Urania, a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees. The framework employs a private clustering mechanism and innovative keyword extraction methods, including frequency-based, TF-IDF-based, and LLM-guided approaches. By leveraging DP tools such as clustering, partition selection, and histogram-based summarization, Urania provides end-to-end privacy protection. Our evaluation assesses lexical and semantic content preservation, pair similarity, and LLM-based metrics, benchmarking against a non-private method inspired by CLIO (Tamkin et al., 2024). Moreover, we develop a simple empirical privacy evaluation that demonstrates the enhanced robustness of our DP pipeline. The results show the framework’s ability to extract meaningful conversational insights while maintaining stringent user privacy, effectively balancing data utility with privacy preservation.
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We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most t of its neighbors (where t is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.
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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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Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
Lynn Chua
Yangsibo Huang
Pritish Kamath
Amer Sinha
Chulin Xie
Chiyuan Zhang
COLM (2025)
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Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, i.e., be crosslingual? This study evaluates state-of-the-art LLMs on inherently crosslingual tasks. We observe that while these models show promising surface-level crosslingual abilities on machine translation and embedding space analyses, they struggle with deeper crosslingual knowledge transfer, revealing a crosslingual knowledge barrier in both general (MMLU benchmark) and domain-specific (Harry Potter quiz and TOFU benchmark) contexts. Since simple inference-time mitigation methods offer only limited improvement, we propose fine-tuning of LLMs on mixed-language data, which effectively reduces these gaps, even when using out-of-domain datasets like WikiText. Our findings suggest the need for explicit optimization to unlock the full crosslingual potential of LLMs. Our code is available at https://github.com/google-research/crosslingual-knowledge-barriers.
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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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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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Balls-and-Bins Sampling for DP-SGD
Lynn Chua
Charlie Harrison
Pritish Kamath
Ethan Leeman
Amer Sinha
Chiyuan Zhang
AISTATS (2025)
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We introduce the Balls-and-Bins sampling for differentially private (DP) optimization methods such as DP-SGD. While it has been common practice to use some form of shuffling in DP-SGD implementations, privacy accounting algorithms have typically assumed that Poisson subsampling is used instead. Recent work by Chua et al. (2024) however pointed out that shuffling based DP-SGD can have a much larger privacy cost in practical regime of parameters. We show that the Balls-and-Bins sampling achieves the “best-of-both” samplers, namely, the implementation of Balls-and-Bins sampling is similar to that of Shuffling and models trained with Balls-and-Bins based DP-SGD achieve utility comparable to those trained with Shuffle based DP-SGD at the same noise multiplier, and yet, Balls-and-Bins sampling enjoys similar-or-better privacy amplification as compared to Poisson subsampling.
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