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Found 5 papers, 0 papers with code
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern.
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy
We point out a major challenge in this problem setting: that common mechanisms for enforcing DP in deep learning, which require injecting \textit{real-valued noise}, are fundamentally incompatible with MPC, which exchanges \textit{finite-field integers} among the participants.
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures.
PrivFT: Private and Fast Text Classification with Homomorphic Encryption
In the second part, we show how to train a model using fully encrypted data to generate an encrypted model.
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs
In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs.
