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Found 5 papers, 2 papers with code
An Efficient and Multi-private Key Secure Aggregation for Federated Learning
With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of the local training data of each client.
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks.
Fingerprinting Generative Adversarial Networks
In this paper, we present the first fingerprinting scheme for the Intellectual Property (IP) protection of GANs.
Scalar Product Lattice Computation for Efficient Privacy-preserving Systems
Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information.
Cryptography and Security
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).
