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20232026
most citedStealing Part of a Production Language Model

7 citations · 11 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CR2026

Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs

Shruthi Gorantala, Jianming Tong, Asra Ali +7

The deployment of Fully Homomorphic Encryption (FHE) at scale is hindered due to its heavy computational overhead. While specialized hardware accelerators like Google Tensor Proces…

cs.CR2025

Privately Estimating Black-Box Statistics

Günter F. Steinke, Thomas Steinke

Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. Bu…

cs.CR2025

Hush! Protecting Secrets During Model Training: An Indistinguishability Approach

Arun Ganesh, Brendan McMahan, Milad Nasr +2

We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contai…

cs.CR2024

Near Exact Privacy Amplification for Matrix Mechanisms

Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque +2

We study the problem of computing the privacy parameters for DP machine learning when using privacy amplification via random batching and noise correlated across rounds via a corre…

cs.CR2024★ 1 cited

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD

Thomas Steinke, Milad Nasr, Arun Ganesh +7

We propose a simple heuristic privacy analysis of noisy clipped stochastic gradient descent (DP-SGD) in the setting where only the last iterate is released and the intermediate ite…

cs.CR2024

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition

Christian Janos Lebeda, Matthew Regehr, Gautam Kamath +1

We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the pri…

cs.CR2024★ 7 cited

Stealing Part of a Production Language Model

Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham +11

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Speci…