MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

• Published in: IEEE access Vol. 7; pp. 29344 - 29354
• Main Authors: Kwabena, Owusu-Agyemang, Qin, Zhen, Zhuang, Tianming, Qin, Zhiguang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation; Artificial neural networks; Cloud computing; Computational modeling; Cryptography; Data models; Deep learning; Encryption; fully homomorphic encryption; Internet of Things; Neural networks; Polynomials; Privacy; privacy-preserving; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2901219

Privacy in the Internet of Things is a fundamental challenge for the Ubiquitous healthcare systems that depend on the data aggregated and collaborative deep learning among different parties. This paper proposes the MSCryptoNet, a novel framework that enables the scalable execution and the conversion of the state-of-the-art learned neural network to the MSCryptoNet models in the privacy-preservation setting. We also design a method for approximation of the activation function basically used in the convolutional neural network (i.e., Sigmoid and Rectified linear unit) with low degree polynomials, which is vital for computations in the homomorphic encryption schemes. Our model seems to target the following scenarios: 1) the practical way to enforce the evaluation of classifier whose inputs are encrypted with possibly different encryption schemes or even different keys while securing all operations including intermediate results and 2) the minimization of the communication and computational cost of the data providers. The MSCryptoNet is based on the multi-scheme fully homomorphic encryption. We also prove that the MSCryptoNet as a privacy-preserving deep learning scheme over the aggregated encrypted data is secured.