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Found 9 papers, 5 papers with code
Summary Statistic Privacy in Data Sharing
We study a setting where a data holder wishes to share data with a receiver, without revealing certain summary statistics of the data distribution (e. g., mean, standard deviation).
On the Privacy Properties of GAN-generated Samples
By drawing connections to the generalization properties of GANs, we prove that under some assumptions, GAN-generated samples inherently satisfy some (weak) privacy guarantees.
RareGAN: Generating Samples for Rare Classes
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget.
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features.
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs).
Enhancing the Privacy of Federated Learning with Sketching
Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties.
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Many existing works treat these concerns separately.
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.
Practical Verifiable In-network Filtering for DDoS defense
In this paper, we show the technical feasibility of verifiable in-network filtering, called VIF, that offers filtering verifiability to DDoS victims and neighbor ASes.
Cryptography and Security
