S. Kumar and M. Singh, Big data analytics for healthcare industry: Impact, applications, and tools, Big Data Mining and Analytics, vol. 2, no. 1, pp. 48–57, 2019.
W. Zhong, N. Yu, and C. Y. Ai, Applying big data based deep learning system to intrusion detection, Big Data Mining and Analytics, vol. 3, no. 3, pp. 181–195, 2020.
H. Nieto-Chaupis, Face to face with next flu pandemic with a wiener-series-based machine learning: Fast decisions to tackle rapid spread, in Proc. 2019 IEEE 9th Annu. Computing and Communication Workshop and Conf., Las Vegas, NV, USA, 2019, pp. 654–658.
B. Adhikari, X. F. Xu, N. Ramakrishnan, and B. A. Prakash, EpiDeep: Exploiting embeddings for epidemic forecasting, in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 577–586.
Z. B. He, Y. S. Li, J. Li, K. Y. Li, Q. Cai, and Y. Liang, Achieving differential privacy of genomic data releasing via belief propagation, Tsinghua Science and Technology, vol. 23, no. 4, pp. 389–395, 2018.
X. Zheng, Z. P. Cai, and Y. S. Li, Data linkage in smart internet of things systems: A consideration from a privacy perspective, IEEE Communications Magazine, vol. 56, no. 9, pp. 55–61, 2018.
Z. P. Cai, Z. B. He, X. Guan, and Y. S. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 4, pp. 577–590, 2018.
A. Croatti, M. Gabellini, S. Montagna, and A. Ricci, On the integration of agents and digital twins in healthcare, J. Med. Syst., vol. 44, no. 9, p. 161, 2020.
N. Bagaria, F. Laamarti, H. F. Badawi, A. Albraikan, R. A. M. Velazquez, and A. El-Saddik, Health 4.0: Digital twins for health and well-being, in Connected Health in Smart Cities, A. El-Saddik, M. S. Hossain, and B. Kantarci, eds. Switzerland: Springer, 2020, pp. 143–152.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, Communication-efficient learning of deep networks from decentralized data, in Proc. 20th Int. Conf. Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.
J. J. Pang, Y. Huang, Z. Z. Xie, Q. L. Han, and Z. P. Cai, Realizing the heterogeneity: A self-organized federated learning framework for IoT, IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3088–3098, 2021.
J. J. Li, H. H. Jiao, J. Wang, Z. G. Liu, and J. Wu, Online real-time trajectory analysis based on adaptive time interval clustering algorithm, Big Data Mining and Analytics, vol. 3, no. 2, pp. 131–142, 2020.
K. Yang, J. H. Zhu, and X. Guo, POI neural-rec model via graph embedding representation, Tsinghua Science and Technology, vol. 26, no. 2, pp. 208–218, 2021.
Z. Wang, C. K. Wang, X. J. Ye, J. S. Pei, and B. Li, Propagation history ranking in social networks: A causality-based approach, Tsinghua Science and Technology, vol. 25, no. 2, pp. 161–179, 2020.
H. X. Chen, S. Feng, X. Pei, Z. Zhang, and D. Y. Yao, Dangerous driving behavior recognition and prevention using an autoregressive time-series model, Tsinghua Science and Technology, vol. 22, no. 6, pp. 682–690, 2017.
Z. L. Ye, H. X. Zhao, K. Zhang, Z. Y. Wang, and Y. Zhu, Network representation based on the joint learning of three feature views, Big Data Mining and Analytics, vol. 2, no. 4, pp. 248–260, 2019.
L. J. Wang, J. Z. Chen, and M. Marathe, DEFSI: Deep learning based epidemic forecasting with synthetic information, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 9607–9612, 2019.
L. Zhao, J. Z. Chen, F. Chen, W. Wang, C. T. Lu, and N. Ramakrishnan, SimNest: Social media nested epidemic simulation via online semi-supervised deep learning, in Proc. 2015 IEEE Int. Conf. Data Mining, Atlantic City, NJ, USA, 2015, pp. 639–648.
B. Y. Shi, J. N. Zhong, Q. Bao, H. J. Qiu, and J. M. Liu, EpiRep: Learning node representations through epidemic dynamics on networks, in Proc. 2019 IEEE/WIC/ACM Int. Conf. Web Intelligence, Thessaloniki, Greece, 2019, pp. 486–492.
Y. Z. Zhou, D. Zhang, and N. X. Xiong, Post-cloud computing paradigms: a survey and comparison, Tsinghua Science and Technology, vol. 22, no. 6, pp. 714–732, 2017.
H. Yang, F. Li, D. X. Yu, Y. F. Zou, and J. G. Yu, Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment, Tsinghua Science and Technology, vol. 26, no. 2, pp. 230–238, 2021.
F. S. Lu, M. W. Hattab, C. L. Clemente, M. Biggerstaff, and M. Santillana, Improved state-level influenza nowcasting in the United States leveraging internet-based data and network approaches, Nature Communications, vol. 10, p. 147, 2019.
B. Zou, V. Lampos, and I. Cox, Multi-task learning improves disease models from web search, in Proc. 2018 World Wide Web Conf., Lyon, France, 2018, pp. 87–96.
Z. P. Cai and X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 2, pp. 766–775, 2020.
Z. P. Cai, X. Zheng, and J. G. Yu, A differential-private framework for urban traffic flows estimation via taxi companies, IEEE Transactions on Industrial Informatics, vol. 15, no. 12, pp. 6492–6499, 2019.
C. Fan, Y. C. Jiang, and A. Mostafavi, Social sensing in disaster city digital twin: Integrated textual–visual–geo framework for situational awareness during built environment disruptions, Journal of Management in Engineering, vol. 36, no. 3, p. 04020002, 2020.
C. Fan, C. Zhang, A. Yahja, and A. Mostafavi, Disaster city digital twin: A vision for integrating artificial and human intelligence for disaster management, International Journal of Information Management, vol. 56, p. 102049, 2021.
A. Francisco, N. Mohammadi, and J. E. Taylor, Smart city digital twin–enabled energy management: Toward real-time urban building energy benchmarking, Journal of Management in Engineering, vol. 36, no. 2, p. 04019045, 2020.
F. Xue, W. S. Lu, Z. Chen, and C. J. Webster, From LiDAR point cloud towards digital twin city: Clustering city objects based on gestalt principles, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 418–431, 2020.
W. J. Holstein, Virtual Singapore-creating an intelligent 3D model to improve experiences of residents, business and government, http://www.3dexperiencecity.com/, 2016.
F. Dembski, U. Wössner, M. Letzgus, M. Ruddat, and C. Yamu, Urban digital twins for smart cities and citizens: The case study of Herrenberg, Germany, Sustainability, vol. 12, no. 6, p. 2307, 2020.
Z. P. Cai and Z. B. He, Trading private range counting over big IoT data, in Proc. 39th IEEE Int. Conf. Distributed Computing Systems, Dallas, TX, USA, 2019, pp. 144–153.
X. Zheng and Z. P. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Areas Commun., vol. 38, no. 5, pp. 968–979, 2020.
A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, Federated learning for mobile keyboard prediction, arXiv preprint arXiv: 1811.03604, 2018.
Y. Q. Chen, X. Qin, J. D. Wang, C. H. Yu, and W. Gao, FedHealth: A federated transfer learning framework for wearable healthcare, IEEE Intell. Syst., vol. 35, no. 4, pp. 83–93, 2020.
G. D. Long, Y. Tan, J. Jiang, and C. Q. Zhang, Federated learning for open banking, in Federated Learning. Lecture Notes in Computer Science, vol 12500, Q. Yang, L. X. Fan, and H. Yu, eds. Switzerland: Springer, 2020, pp. 240–254.
S. J. Bai, J. Z. Kolter, and V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv: 1803.01271, 2018.
N. Mohammadi and J. E. Taylor, Smart city digital twins, in Proc. 2017 IEEE Symp. Series on Computational Intelligence, Honolulu, HI, USA, 2017, pp. 1–5.
T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, and J. Passerat-Palmbach, A generic framework for privacy preserving deep learning, arXiv preprint arXiv: 1811.04017, 2018.