Private Collaborative Neural Network Learning

By Melissa Chase, Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal. 2017

73 citations recorded by the source corpus.

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Citations in the corpus

Privacy in Deep Learning: A Survey (2020, methodology): “w/ DNN Distributed DP [87] # # eICU, TCGA Classification w/ DNN DP model publishing [88] # # MNIST, CIFAR Image Classification w/ DNN DP federated learning [89] # # MNIST Image Classification w/ DNN ScalarDP, PrivUnit [90] # # MNIST, CIFAR Image Classification w/ DNN DSSGD [21] # # MNIST, SVHN Image”
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018, background): “[9] also achieve similar bound on noise in distributed learning setting, their method considers only the convex case.”
Secure Byzantine-Robust Machine Learning (2020, background): “Global differential privacy (DP) [35, 1, 8] is mainly concerned about the privacy of the aggregated model, and whether it leaks information about the training data.”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “Chase et al. [22] consider training neural networks using a hybrid of secure computation and diﬀerential privacy.”
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service (2018, background): “Since the initial work on BNNs there has been a wealth of work on binarizing, ternarizing, and quantizing neural networks Chen et al. (2015); Courbariaux et al. (2015); Han et al. (2016); Hubara et al.”
EPIC: Efficient Private Image Classification (or: Learning from the Masters) (2019, methodology): “[9] combine MPC techniques with differential privacy techniques to achieve private neural network learning.”