6.3k citations · 7.3k across the 9 of their papers we have counts for
9 papers
Oneshot Differentially Private Top-k Selection
Gang Qiao, Weijie J. Su, Li Zhang
Being able to efficiently and accurately select the top- elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we pr…
Rényi Differential Privacy of the Sampled Gaussian Mechanism
Ilya Mironov, Kunal Talwar, Li Zhang
The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications. The m…
Differentially Private False Discovery Rate Control
Cynthia Dwork, Weijie J. Su, Li Zhang
Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false di…
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar +1
We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our…
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Martín Abadi, Úlfar Erlingsson, Ian Goodfellow +5
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving priv…
Deep Learning with Differential Privacy
Martín Abadi, Andy Chu, Ian Goodfellow +4
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative dat…
Private False Discovery Rate Control
Cynthia Dwork, Weijie Su, Li Zhang
We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certai…
Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry
Kunal Talwar, Abhradeep Thakurta, Li Zhang
Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set. When the training datase…
The Geometry of Differential Privacy: the Sparse and Approximate Cases
Aleksandar Nikolov, Kunal Talwar, Li Zhang
In this work, we study trade-offs between accuracy and privacy in the context of linear queries over histograms. This is a rich class of queries that includes contingency tables an…