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11/08/2022
Centaur: Federated Learning for Constrained Edge Devices
Federated learning (FL) on deep neural networks facilitates new applicat...
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08/22/2022
SoK: Machine Learning with Confidential Computing
Privacy and security challenges in Machine Learning (ML) have become a c...
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05/06/2022
Private delegated computations using strong isolation
Sensitive computations are now routinely delegated to third-parties. In ...
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02/16/2022
Towards Battery-Free Machine Learning and Inference in Underwater Environments
This paper is motivated by a simple question: Can we design and build ba...
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05/28/2021
Quantifying Information Leakage from Gradients
Sharing deep neural networks' gradients instead of training data could f...
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04/29/2021
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
We propose and implement a Privacy-preserving Federated Learning (PPFL) ...
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10/17/2020
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Training a deep neural network (DNN) via federated learning allows parti...
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04/12/2020
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Exec...
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07/13/2019
Towards Characterizing and Limiting Information Exposure in DNN Layers
Pre-trained Deep Neural Network (DNN) models are increasingly used in sm...
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07/02/2018
