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Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Secure aggregation is a critical component in federated learning, which ...
06/07/2021 ∙ by Jinhyun So, et al. ∙
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On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Outsourcing neural network inference tasks to an untrusted cloud raises ...
11/11/2020 ∙ by Ramy E. Ali, et al. ∙
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A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
We consider a collaborative learning scenario in which multiple data-own...
11/03/2020 ∙ by Jinhyun So, et al. ∙
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Byzantine-Resilient Secure Federated Learning
Secure federated learning is a privacy-preserving framework to improve m...
07/21/2020 ∙ by Jinhyun So, et al. ∙
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Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is gaining significant interests as it enables model ...
02/11/2020 ∙ by Jinhyun So, et al. ∙
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CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
How to train a machine learning model while keeping the data private and...
02/02/2019 ∙ by Jinhyun So, et al. ∙
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Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a distributed computing scenario that involves computations ...
06/04/2018 ∙ by Qian Yu, et al. ∙
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