• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 15, No. 12, December 31, 2021
10.3837/tiis.2021.12.005, Download Paper (Free):

Abstract

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users’ images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties’ data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.


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Cite this article

[IEEE Style]
Y. Bai, Y. Feng and W. Wu, "Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing," KSII Transactions on Internet and Information Systems, vol. 15, no. 12, pp. 4345-4363, 2021. DOI: 10.3837/tiis.2021.12.005.

[ACM Style]
Yanan Bai, Yong Feng, and Wenyuan Wu. 2021. Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing. KSII Transactions on Internet and Information Systems, 15, 12, (2021), 4345-4363. DOI: 10.3837/tiis.2021.12.005.

[BibTeX Style]
@article{tiis:25142, title="Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing", author="Yanan Bai and Yong Feng and Wenyuan Wu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2021.12.005}, volume={15}, number={12}, year="2021", month={December}, pages={4345-4363}}