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Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

Deep learning in robot systems is a popular application that can learn and train the results per requirements, but that collects sensitive information in the training process, easily causing leakage of users’ private information. To date, privacy-preserving deep learning models in robot systems have been sparsely researched. To solve the privacy leakage problem of deep learning in robot systems and fill the gap in robotics deep learning privacy research, in this paper a novel privacy-preserving image multi-classification deep-learning (PIDL) model in robot systems is presented. In PIDL, two schemes are proposed that adopt two groups of encrypted activation and cost functions—sigmoid plus cross-entropy function (PIDLSC) and softmax plus log-likelihood function (PIDLSL)—with secure calculation protocols, which are applied in a fog control center (FCC) with a non-colluding honest server by homomorphic encryption to improve the training efficiency, solve the encryption computation questions, and protect data and model privacy in robot systems. Security analysis and performance evaluation demonstrate that the proposed schemes realize security, correctness, and efficiency with low communication and computational costs.

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Acknowledgements

This research was funded by the National Key R&D Program of China under Grant No. 2017YFB0802000, the National Natural Science Foundation of China under Grant Nos. U19B2021, U1736111, 61972457, the National Cryptography Development Fund under Grant No. MMJJ20180111, Key Technologies R&D Program of Henan Province under Grant No. 192102210295, Key Research and Development Program of Shaanxi under Grant No. 2020ZDLGY08-04, the Program for Science & Technology Innovation Talents in Universities of Henan Province under Grant No. 18HASTIT022.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, China

    Yange Chen, Baocang Wang & SuYu He

  2. School of Information Engineering, Xuchang University, Xuchang, China

    Yange Chen, Yuan Ping, Zhili Zhang & Baocang Wang

  3. Cryptographic Research Center, Xidian University, Xi’an, China

    Baocang Wang & SuYu He

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  1. Yange Chen
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Correspondence to Zhili Zhang.

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Chen, Y., Ping, Y., Zhang, Z. et al. Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT. Neural Comput & Applic 33, 4677–4694 (2021). https://doi.org/10.1007/s00521-020-05426-0

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