POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, João Sá Sousa, Jean‐Pierre Hubaux
Abstract
Furthermore, the trusted party becomes a single point of failure, thus both data and model privacy could be compromised by data breaches, hacking, leaks, etc. Hence, solutions originating from the cryptographic community replace and emulate the trusted party with a group of computing servers. In particular, to enable privacy-preserving training of NNs, several studies employ multiparty computation (MPC) techniques and operate on the two [83], [28], three [82], [110],
Topics & Concepts
Computer scienceCryptographyArtificial neural networkMNIST databaseHomomorphic encryptionEncryptionComputationOverhead (engineering)Secure multi-party computationComputer networkDistributed computingTheoretical computer scienceArtificial intelligenceComputer securityAlgorithmOperating systemPrivacy-Preserving Technologies in DataCryptography and Data SecurityAdversarial Robustness in Machine Learning