7 citations · 11 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla +2
In the task of differentially private (DP) continual counting, we receive a stream of increments and our goal is to output an approximate running total of these increments, without…
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…
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…