Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning

By Qingchen Zhang, Laurence Tianruo Yang, Zhikui Chen. 2016

261 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Federated Machine Learning (2019, neutral): “Recent works adopted homomorphic encryption for centralizing and training data on cloud [75, 76].”
CryptoDL: Deep Neural Networks over Encrypted Data (2017, methodology): “There are also a few recent work that look at privacy issues in training phase, specifically for back-propagation algorithm [47,48].”
Privacy-preserving outsourced classification in cloud computing (2017, methodology): “Zhang et al. [18] utilized a fully homomorphic property (BGV cryptosystem [19]) to perform the outsourcing computation.”
Differential Privacy for Deep and Federated Learning: A Survey (2022, methodology): “[28] when the model is already trained and ready to use.”
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].”
Hardware-Assisted Intellectual Property Protection of Deep Learning Models (2020, background): “There has been a lot of research to address the privacy concerns of user data which are used to train Deep Neural Networks (DNNs) [6], [21], [27].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Cryptographic methods such as homomorphic encryption [15, 77, 27, 33, 74, 145, 146, 204, 206, 112], and secure multi-party computation (SMC) [157, 36, 23, 51, 24, 101, 17, 62, 91, 181, 39, 65] are widely used in privacy-preserving machine learning algorithms.”
A survey on deep learning for big data (2018, background): “[85,86] presented a tensor deep learning model, called deep computation model, for heterogeneous data.”
Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning (2017, methodology): “For minimizing the value of C ð uu Þ in the backpropagation process, we use the gradient descent of C ð uu Þ to update uu as shown in Equation (19) [33] uu : ¼ uu (cid”