157 citations · 741 across the 64 of their papers we have counts for
16 papers · 1 filter
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…
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…
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…
Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models
Hongbin Liu, Lun Wang, Om Thakkar +2
Large ASR models can inadvertently leak sensitive information, which can be mitigated by formal privacy measures like differential privacy (DP). However, traditional DP training is…
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…
Improved Differentially Private and Lazy Online Convex Optimization
Naman Agarwal, Satyen Kale, Karan Singh +1
We study the task of -differentially private online convex optimization (OCO). In the online setting, the release of each distinct decision or iterate carries with it the p…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
Differentially Private Stream Processing at Scale
Bing Zhang, Vadym Doroshenko, Peter Kairouz +6
We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-S…
Fully Adaptive Composition for Gaussian Differential Privacy
Adam Smith, Abhradeep Thakurta
We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a ful…
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz, Brendan McMahan, Shuang Song +3
We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), r…
Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg +6
A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algori…
Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent
Shuang Song, Thomas Steinke, Om Thakkar +1
We revisit the well-studied problem of differentially private empirical risk minimization (ERM). We show that for unconstrained convex generalized linear models (GLMs), one can obt…
Guidelines for Implementing and Auditing Differentially Private Systems
Daniel Kifer, Solomon Messing, Aaron Roth +2
Differential privacy is an information theoretic constraint on algorithms and code. It provides quantification of privacy leakage and formal privacy guarantees that are currently c…
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +4
Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to compr…
Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy
Kashyap Dixit, Madhav Jha, Abhradeep Thakurta
In this work, we present a connection between Lipschitz property testing and a relaxed notion of differential privacy, where we assume that the datasets are being sampled from a do…