1 code implementation • 3 Mar 2023 • Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
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).
no code implementations • 3 Jun 2022 • Zinan Lin, Vyas Sekar, Giulia Fanti
By drawing connections to the generalization properties of GANs, we prove that under some assumptions, GAN-generated samples inherently satisfy some (weak) privacy guarantees.
1 code implementation • 20 Mar 2022 • Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget.
no code implementations • 22 Jan 2021 • Todd Huster, Jeremy E. J. Cohen, Zinan Lin, Kevin Chan, Charles Kamhoua, Nandi Leslie, Cho-Yu Jason Chiang, Vyas Sekar
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.
1 code implementation • NeurIPS 2021 • Zinan Lin, Vyas Sekar, Giulia Fanti
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs).
no code implementations • 5 Nov 2019 • Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar
Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties.
no code implementations • 3 Nov 2019 • Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith
Many existing works treat these concerns separately.
4 code implementations • 30 Sep 2019 • Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, Vyas Sekar
By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.
1 code implementation • 4 Jan 2019 • Deli Gong, Muoi Tran, Shweta Shinde, Hao Jin, Vyas Sekar, Prateek Saxena, Min Suk Kang
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