scholarly journals Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing

2021 ◽  
Vol 15 (12) ◽  
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Convolutional Neural Network ◽  
Mobile Cloud Computing ◽  
Privacy Preserving ◽  
Mobile Cloud ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU

2021 ◽  
Vol 15 ◽  
Author(s):  
Yanan Bai ◽  
Quanliang Liu ◽  
Wenyuan Wu ◽  
Yong Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Homomorphic Encryption ◽  
Matrix Multiplication ◽  
Privacy Preserving ◽  
Superior Performance ◽  
Optimization Approach ◽  
Computation Efficiency ◽  
Neural Network Prediction ◽  
Network Prediction

The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure client and model-holder data privately on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with a larger-scale dataset, due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of a convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is 2-fold: (1) To avoid the trivia and complicated homomorphic matrix computations brought by BGV-based solutions, it adopts GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. (2) To improve the computation efficiency on GPU, a hybrid optimization approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the other frameworks.

Non-interactive privacy-preserving neural network prediction

2019 ◽  
Vol 481 ◽  
pp. 507-519 ◽  
Author(s):  
Xu Ma ◽  
Xiaofeng Chen ◽  
Xiaoyu Zhang
Keyword(s):  
Neural Network ◽  
Privacy Preserving ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals Offline privacy preserving proxy re-encryption in mobile cloud computing

2019 ◽  
Vol 59 ◽  
pp. 101081 ◽  
Author(s):  
Arthur Sandor Voundi Koe ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Privacy Preserving ◽  
Mobile Cloud
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