466 citations · 1.6k across the 73 of their papers we have counts for
9 papers · 1 filter
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms
Zeyu Ding, Yuxin Wang, Yingtai Xiao +3
Private selection algorithms, such as the Exponential Mechanism, Noisy Max and Sparse Vector, are used to select items (such as queries with large answers) from a set of candidates…
The Neural Coding Framework for Learning Generative Models
Alexander Ororbia, Daniel Kifer
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computati…
Optimizing Fitness-For-Use of Differentially Private Linear Queries
Yingtai Xiao, Zeyu Ding, Yuxin Wang +2
In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each…
Differentially Private Deep Learning with Direct Feedback Alignment
Jaewoo Lee, Daniel Kifer
Standard methods for differentially private training of deep neural networks replace back-propagated mini-batch gradients with biased and noisy approximations to the gradient. Thes…
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
Yuxin Wang, Zeyu Ding, Daniel Kifer +1
We propose CheckDP, the first automated and integrated approach for proving or disproving claims that a mechanism is differentially private. CheckDP can find counterexamples for me…
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Jaewoo Lee, Daniel Kifer
Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private…