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Blockchain-enabled Tensor-based Conditional Deep Convolutional GAN for Cyber-physical-Social Systems

Published: 21 June 2021 Publication History

Abstract

Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.

References

[1]
Shubhani Aggarwal, Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Kim-Kwang Raymond Choo, and Albert Y. Zomaya. 2019. Blockchain for smart communities: Applications, challenges and opportunities. J. Netw. Comput. Applic. (2019).
[2]
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017).
[3]
Kim-Kwang Raymond Choo, Zheng Yan, and Weizhi Meng. 2020. Blockchain in industrial IoT applications: Security and privacy advances, challenges and opportunities. IEEE Trans. Industr. Inf. 16, 6 (2020), 4119–4121.
[4]
Konstantinos Christidis and Michael Devetsikiotis. 2016. Blockchains and smart contracts for the internet of things. IEEE Access 4 (2016), 2292–2303.
[5]
Zihan Ding, Xiaoyang Liu, Miao Yin, Wei Liu, and Linghe Kong. 2019. TGAN: Deep tensor generative adversarial nets for large image generation. arXiv preprint arXiv:1901.09953 (2019).
[6]
Jun Feng, Laurence T. Yang, Nicholaus J. Gati, Xia Xie, and Benard S. Gavuna. 2020. Privacy-preserving computation in cyber-physical-social systems: A survey of the state-of-the-art and perspectives. Inf. Sci. 527 (2020), 341–355.
[7]
Jun Feng, Laurence T. Yang, and Ronghao Zhang. 2019. Practical privacy-preserving high-order Bi-Lanczos in integrated edge-fog-cloud architecture for cyber-physical-social systems. ACM Trans. Internet Technol. 19, 2 (2019), 1–18.
[8]
Jun Feng, Laurence T. Yang, Ronghao Zhang, and Benard Safari Gavuna. 2020. Privacy preserving Tucker train decomposition over blockchain-based encrypted industrial IoT data. IEEE Trans. Industr. Inf. (2020).
[9]
Jun Feng, Laurence T. Yang, Ronghao Zhang, Weizhong Qiang, and Jinjun Chen. 2020. Privacy preserving high-order Bi-Lanczos in cloud-fog computing for industrial applications. IEEE Trans. Industr. Inf. (2020).
[10]
Wei Feng and Zheng Yan. 2019. MCS-Chain: Decentralized and trustworthy mobile crowdsourcing based on blockchain. Fut. Gen. Comput. Syst. 95 (2019), 649–666.
[11]
Ugo Fiore, Alfredo De Santis, Francesca Perla, Paolo Zanetti, and Francesco Palmieri. 2019. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf. Sci. 479 (2019), 448–455.
[12]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2672–2680.
[13]
Mohamed Tahar Hammi, Badis Hammi, Patrick Bellot, and Ahmed Serhrouchni. 2018. Bubbles of trust: A decentralized blockchain-based authentication system for IoT. Comput. Sec. 78 (2018), 126–142.
[14]
Anish Jindal, Gagangeet Singh Singh Aujla, Neeraj Kumar, and Massimo Villari. 2019. GUARDIAN: Blockchain-based secure demand response management in smart grid system. IEEE Trans. Serv. Comput. (2019).
[15]
Nir Kshetri. 2017. Can blockchain strengthen the internet of things?IT Prof. 19, 4 (2017), 68–72.
[16]
Jay Lee, Moslem Azamfar, and Jaskaran Singh. 2019. A blockchain enabled cyber-physical system architecture for industry 4.0 manufacturing systems. Manuf. Lett. 20 (2019), 34–39.
[17]
Ao Lei, Haitham Cruickshank, Yue Cao, Philip Asuquo, Chibueze P. Anyigor Ogah, and Zhili Sun. 2017. Blockchain-based dynamic key-management for heterogeneous intelligent transportation systems. IEEE Internet Things J. 4, 6 (2017), 1832–1843.
[18]
Liangzhi Li, Kaoru Ota, and Mianxiong Dong. 2018. Sustainable CNN for robotic: An offloading game in the 3D vision computation. IEEE Trans. Sustain. Comput. 4, 1 (2018), 67–76.
[19]
Gaoqi Liang, Steven R. Weller, Fengji Luo, Junhua Zhao, and Zhao Yang Dong. 2018. Distributed blockchain-based data protection framework for modern power systems against cyber atacks. IEEE Trans. Smart Grid 10, 3 (2018), 3162–3173.
[20]
Chao Lin, Debiao He, Xinyi Huang, Xiang Xie, and Kim-Kwang Raymond Choo. 2018. Blockchain-based system for secure outsourcing of bilinear pairings. Inf. Sci. (2018).
[21]
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision. 3730–3738.
[22]
Shunli Ma, Yi Deng, Debiao He, Jiang Zhang, and Xiang Xie. 2020. An efficient NIZK scheme for privacy-preserving transactions over account-model blockchain. IEEE Trans. Depend. Sec. Comput. (2020).
[23]
Dennis Miller. 2018. Blockchain and the internet of things in the industrial sector. IT Prof. 20, 3 (2018), 15–18.
[24]
Kaoru Ota, Minhson Dao, Vasileios Mezaris, and Francesco G. B. De Natale. 2017. Deep learning for mobile multimedia: A survey. ACM Trans. Multimedia Comput., Commun., Applic. 13, 3 (2017), 34.
[25]
Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).
[26]
Md Abdur Rahman, Md Mamunur Rashid, M. Shamim Hossain, Elham Hassanain, Mohammed F. Alhamid, and Mohsen Guizani. 2019. Blockchain and IoT-based cognitive edge framework for sharing economy services in a smart city. IEEE Access 7 (2019), 18611–18621.
[27]
Eli Ben Sasson, Alessandro Chiesa, Christina Garman, Matthew Green, Ian Miers, Eran Tromer, and Madars Virza. 2014. Zerocash: Decentralized anonymous payments from bitcoin. In Proceedings of the IEEE Symposium on Security and Privacy. 459–474.
[28]
Rafał Skowroński. 2019. The open blockchain-aided multi-agent symbiotic cyber physical systems. Fut. Gen. Comput. Syst. 94 (2019), 430–443.
[29]
Qingquan Song, Hancheng Ge, James Caverlee, and Xia Hu. 2019. Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13, 1 (2019), 1–48.
[30]
Jinjun Tang, Xiaolu Wang, Fang Zong, and Zheng Hu. 2020. Uncovering spatio-temporal travel patterns using a tensor-based model from metro smart card data in Shenzhen, China. Sustainability 12, 4 (2020), 1475.
[31]
Yuxuan Wang, Fengji Luo, Zhaoyang Dong, Ziyuan Tong, and Yichen Qiao. 2019. Distributed meter data aggregation framework based on blockchain and homomorphic encryption. IET Cyber-phys. Syst.: Theor. Applic. 4, 1 (2019), 30–37.
[32]
Jun Wu, Mianxiong Dong, Kaoru Ota, Jianhua Li, and Wu Yang. 2020. Application-aware consensus management for software-defined intelligent blockchain in IoT. IEEE Netw. 34, 1 (2020), 69–75.
[33]
Wenjie Yang, Xingang Liu, Lan Zhang, and Laurence T. Yang. 2013. Big data real-time processing based on storm. In Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom’13). 1784–1787.
[34]
Abbas Yazdinejad, Reza M. Parizi, Ali Dehghantanha, Qi Zhang, and Kim-Kwang Raymond Choo. 2020. An energy-efficient SDN controller architecture for IoT networks with blockchain-based security. IEEE Trans. Serv. Comput. (2020).
[35]
Yong Yu, Yannan Li, Junfeng Tian, and Jianwei Liu. 2018. Blockchain-based solutions to security and privacy issues in the internet of things. IEEE Wirel. Commun. 25, 6 (2018), 12–18.
[36]
Jing Zeng, Laurence T. Yang, Man Lin, Huansheng Ning, and Jianhua Ma. 2020. A survey: Cyber-physical-social systems and their system-level design methodology. Fut. Gen. Comput. Syst. 105 (2020), 1028–1042.
[37]
Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li. 2017. PPHOPCM: Privacy-preserving high-order possibilistic c-means algorithm for big data clustering with cloud computing. IEEE Trans. Big Data (2017).
[38]
Ming Zheng, Tong Li, Rui Zhu, Yahui Tang, Mingjing Tang, Leilei Lin, and Zifei Ma. 2020. Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification. Inf. Sci. 512 (2020), 1009–1023.
[39]
Zhenyu Zhou, Bingchen Wang, Mianxiong Dong, and Kaoru Ota. 2020. Secure and efficient vehicle-to-grid energy trading in cyber physical systems: Integration of blockchain and edge computing. IEEE Trans. Syst., Man, Cyber.: Syst. 50, 1 (2020), 43–57.
[40]
Chunsheng Zhu, Joel J. P. C. Rodrigues, Victor C. M. Leung, Lei Shu, and Laurence T. Yang. 2018. Trust-based communication for the industrial internet of things. IEEE Commun. Mag. 56, 2 (2018), 16–22.

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        Published In

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 21, Issue 2
        June 2021
        599 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3453144
        • Editor:
        • Ling Liu
        Issue’s Table of Contents
        ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        Association for Computing Machinery

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        Publication History

        Published: 21 June 2021
        Accepted: 01 May 2020
        Revised: 01 May 2020
        Received: 01 November 2019
        Published in TOIT Volume 21, Issue 2

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        Author Tags

        1. Cyber-physical-social systems
        2. deep learning
        3. generative adversarial network
        4. tensor
        5. blockchain

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        • Research-article
        • Refereed

        Funding Sources

        • National Key R&D Program of China
        • National Natural Science Foundation of China
        • China Postdoctoral International Exchange Fellowship Program
        • Fundamental Research Funds for the Central Universities in China

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