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On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Outsourcing neural network inference tasks to an untrusted cloud raises ...
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A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
We consider a collaborative learning scenario in which multiple data-own...
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Byzantine-Resilient Secure Federated Learning
Secure federated learning is a privacy-preserving framework to improve m...
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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 ...
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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...
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Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a distributed computing scenario that involves computations ...
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Jinhyun So
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