Search Results for author:
Found 9 papers, 1 papers with code
DP-REC: Private & Communication-Efficient Federated Learning
Privacy and communication efficiency are important challenges in federated training of neural networks, and combining them is still an open problem.
Unsupervised Information Obfuscation for Split Inference of Neural Networks
In our method, the edge device runs the model up to a split layer determined based on its computational capacity.
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings
We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly large settings (e. g., resource allocation in urban environments, mobility-on-demand systems, etc.
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples form a generative adversarial network.
Federated Learning with Bayesian Differential Privacy
We consider the problem of reinforcing federated learning with formal privacy guarantees.
Federated Generative Privacy
In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting.
Bayesian Differential Privacy for Machine Learning
Traditional differential privacy is independent of the data distribution.
Generating Artificial Data for Private Deep Learning
In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Generating Differentially Private Datasets Using GANs
In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data.
BIG-bench Machine Learning
Generative Adversarial Network
+1
