POSEIDON: Privacy-Preserving Federated Neural Network Learning

Understand

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an $N$-party, federated learning setting.

• We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network training.
• It employs multiparty lattice-based cryptography to preserve the confidentiality of the training data, the model, and the evaluation data, under a passive-adversary model and collusions between up to $N-1$ parties.
• To efficiently execute the secure backpropagation algorithm for training neural networks, we provide a generic packing approach that enables Single Instruction, Multiple Data (SIMD) operations on encrypted data.