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Found 5 papers, 1 papers with code
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
We also develop a novel aggregation scheme within the encrypted domain, utilizing users' non-poisoning rates, to effectively address data poisoning attacks while ensuring privacy is preserved by the proposed encryption scheme.
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning
However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference.
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.
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
Spontaneous expression classification in the encrypted domain
To date, most facial expression analysis have been based on posed image databases and is carried out without being able to protect the identity of the subjects whose expressions are being recognised.
