Privacy-Preserving Backpropagation Neural Network Learning

By Tingting Chen, Sheng Zhong. 2009

130 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neurology

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Citations in the corpus

Learning Anonymized Representations with Adversarial Neural Networks (2018, background): “The literature in statistics and computer science on anonymization and privacy is extensive; we discuss only directly relevant work here (see [Chen et al., 2009] and references therein).”
A privacy-preserving algorithm for distributed training of neural network ensembles (2012, methodology): “, [4–6]) that provide practical privacy-preserving training algorithms for neural network, we simply take the solution from one [6] of them as the privacy-preserving neural network training algorithm in our algorithm.”
Secure Multilayer Perceptron Based On Homomorphic Encryption (2018, background): “promise between accuracy and privacy. Higher the number of shared parameters, better is the classication accuracy but lower is privacy. To go further, and in addition to the secure NN weight update, [9] aims at securing the NN activation function with additive secret sharing. Because activation func”
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, result): “the source data and the intermediate results [53].”
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (2019, background): “been around in the past few years [8], [9], [10], [11], [12], [13], [14], [15], [16].”
Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing (2014, background): “Chen and Zhong [6] propose a privacy preserving BPN network learning algorithm for two-party scenarios.”