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Tsinghua Science and Technology 2024, 29(4): 1152-1180 https://doi.org/10.26599/TST.2023.9010080
Open Access | Issue | Published: 09 February 2024

A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions

Show Author's Information Ahmad Alzu’bi1( ),  Ala’a Alomar1,  Shahed Alkhaza’leh1,  Abdelrahman Abuarqoub2,  Mohammad Hammoudeh3
Department of Computer Science, Jordan University of Science and Technology, Irbid 22110, Jordan
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia
Keywords:
edge computing, fog computing, data privacy, healthcare systems, intelligent edges
Cite this article:
Alzu’bi A, Alomar A, Alkhaza’leh S, et al. A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions. Tsinghua Science and Technology, 2024, 29(4): 1152-1180. https://doi.org/10.26599/TST.2023.9010080
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Abstract Full text About this article

The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (IoT) and edge computing, there is a greater need than ever to adequately monitor the data being acquired, shared, processed, and stored. The growth of cloud, IoT, and edge computing models presents severe data privacy concerns, especially in the healthcare sector. However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking. This paper discusses the current state of privacy-preservation solutions in IoT and edge healthcare applications. It identifies the common strategies often used to include privacy by the intelligent edges and technologies in healthcare systems. Furthermore, the study addresses the technical complexity, efficacy, and sustainability limits of these methods. The study also highlights the privacy issues and current research directions that have driven the IoT and edge healthcare solutions, with which more insightful future applications are encouraged.


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About this article

A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions

Show Author's information Ahmad Alzu’bi1( ), Ala’a Alomar1, Shahed Alkhaza’leh1, Abdelrahman Abuarqoub2, Mohammad Hammoudeh3
Department of Computer Science, Jordan University of Science and Technology, Irbid 22110, Jordan
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia

Abstract

The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (IoT) and edge computing, there is a greater need than ever to adequately monitor the data being acquired, shared, processed, and stored. The growth of cloud, IoT, and edge computing models presents severe data privacy concerns, especially in the healthcare sector. However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking. This paper discusses the current state of privacy-preservation solutions in IoT and edge healthcare applications. It identifies the common strategies often used to include privacy by the intelligent edges and technologies in healthcare systems. Furthermore, the study addresses the technical complexity, efficacy, and sustainability limits of these methods. The study also highlights the privacy issues and current research directions that have driven the IoT and edge healthcare solutions, with which more insightful future applications are encouraged.

Keywords: edge computing, fog computing, data privacy, healthcare systems, intelligent edges

References(150)

[1]
A. Singh and K. Chatterjee, Security and privacy issues of electronic healthcare system: A survey, J. Inform. Optim. Sci., vol. 40, no. 8, pp. 1709–1729, 2019.
DOI
[2]

R. Wang, J. Lai, Z. Zhang, X. Li, P. Vijayakumar, and M. Karuppiah, Privacy-preserving federated learning for internet of medical things under edge computing, IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 854–865, 2023.
DOI Google Scholar
[3]

N. Fernando, S. W. Loke, and W. Rahayu, Mobile cloud computing: A survey, Future Gener. Comput. Syst., vol. 29, no. 1, pp. 84–106, 2013.
DOI Google Scholar
[4]
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, Fog computing and its role in the internet of things, in Proc. first Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 2012, pp. 13–16.
DOI
[5]

K. Gai, M. Qiu, H. Zhao, L. Tao, and Z. Zong, Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing, J. Netw. Comput. Appl., vol. 59, pp. 46–54, 2016.
DOI Google Scholar
[6]

Y. Hao and R. Foster, Wireless body sensor networks for health-monitoring applications, Physiol. Meas., vol. 29, no. 11, pp. R27–R56, 2008.
DOI Google Scholar
[7]

K. Cao, Y. Liu, G. Meng, and Q. Sun, An overview on edge computing research, IEEE Access, vol. 8, pp. 85714–85728, 2020
DOI Google Scholar
[8]

A. Lakhan, M. A. Mohammed, A. N. Rashid, S. Kadry, K. H. Abdulkareem, J. Nedoma, R. Martinek, and I. Razzak, Restricted boltzmann machine assisted secure serverless edge system for internet of medical things, IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 673–683, 2023.
DOI Google Scholar
[9]

J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications, IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, 2017.
DOI Google Scholar
[10]

A. Mosenia and N. K. Jha, A comprehensive study of security of internet-of-things, IEEE Trans. Emerg. Top. Comput., vol. 5, no. 4, pp. 586–602, 2017.
DOI Google Scholar
[11]

X. Zhou, W. Liang, W. Li, K. Yan, S. Shimizu, and K. I. K. Wang, Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system, IEEE Internet Things J., vol. 9, no. 12, pp. 9310–9319, 2022.
DOI Google Scholar
[12]

A. Algarni, A survey and classification of security and privacy research in smart healthcare systems, IEEE Access, vol. 7, pp. 101879–101894, 2019.
DOI Google Scholar
[13]

J. J. Hathaliya and S. Tanwar, An exhaustive survey on security and privacy issues in Healthcare 4.0, Comput. Commun., vol. 153, pp. 311–335, 2020.
DOI Google Scholar
[14]

W. Sun, Z. Cai, Y. Li, F. Liu, S. Fang, and G. Wang, Security and privacy in the medical internet of things: A review, Secur. Commun. Netw., vol. 2018, p. 5978636, 2018.
DOI Google Scholar
[15]

Y. Xiao, Y. Jia, C. Liu, X. Cheng, J. Yu, and W. Lv, Edge computing security: State of the art and challenges, Proc. IEEE, vol. 107, no. 8, pp. 1608–1631, 2019.
DOI Google Scholar
[16]
Statista—the statistics portal, https://www.statista.com/, 2023.
[17]

M. Yahuza, M. Y. I. B. Idris, A. W. B. A. Wahab, A. T. S. Ho, S. Khan, S. N. B. Musa, and A. Z. B. Taha, Systematic review on security and privacy requirements in edge computing: State of the art and future research opportunities, IEEE Access, vol. 8, pp. 76541–76567, 2020.
DOI Google Scholar
[18]

J. Zhang, B. Chen, Y. Zhao, X. Cheng, and F. Hu, Data security and privacy-preserving in edge computing paradigm: Survey and open issues, IEEE Access, vol. 6, pp. 18209–18237, 2018.
DOI Google Scholar
[19]

H. S. G. Pussewalage and V. A. Oleshchuk, Privacy preserving mechanisms for enforcing security and privacy requirements in E-health solutions, Int. J. Inf. Manage., vol. 36, no. 6, pp. 1161–1173, 2016.
DOI Google Scholar
[20]

R. Roman, J. Lopez, and M. Mambo, Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges, Future Gener. Comput. Syst., vol. 78, pp. 680–698, 2018.
DOI Google Scholar
[21]
Z. Huang, G. Xia, Z. Wang, and S. Yuan, Survey on edge computing security, in Proc. Int. Conf. Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Fuzhou, China, 2020, pp. 96–105.
[22]

F. Y. Rao and E. Bertino, Privacy techniques for edge computing systems, Proc. IEEE, vol. 107, no. 8, pp. 1632–1654, 2019.
DOI Google Scholar
[23]

M. Hartmann, U. S. Hashmi, and A. Imran, Edge computing in smart health care systems: Review, challenges, and research directions, Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, p. e3710, 2022.
DOI Google Scholar
[24]

Q. Jiang, X. Zhou, R. Wang, W. Ding, Y. Chu, S. Tang, X. Jia, and X. Xu, Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey, Appl. Soft Comput., vol. 123, p. 108835, 2022.
DOI Google Scholar
[25]

A. Ometov, O. L. Molua, M. Komarov, and J. Nurmi, A survey of security in cloud, edge, and fog computing, Sensors, vol. 22, no. 3, p. 927, 2022.
DOI Google Scholar
[26]

M. D. A. Rahman, M. S. Hossain, G. Loukas, E. Hassanain, S. S. Rahman, M. F. Alhamid, and M. Guizani, Blockchain-based mobile edge computing framework for secure therapy applications, IEEE Access, vol. 6, pp. 72469–72478, 2018.
DOI Google Scholar
[27]

R. Kumar and R. Tripathi, Towards design and implementation of security and privacy framework for internet of medical things (IoMT) by leveraging blockchain and IPFS technology, J. Supercomput., vol. 77, no. 8, pp. 7916–7955, 2021.
DOI Google Scholar
[28]
G. Tripathi, M. Abdul Ahad, and S. Paiva, SMS: A secure healthcare model for smart cities, Electronics, vol. 9, no. 7, p. 1135, 2020.
DOI
[29]

R. Saha, G. Kumar, M. K. Rai, R. Thomas, and S. J. Lim, Privacy ensured e-healthcare for fog-enhanced IoT based applications, IEEE Access, vol. 7, pp. 44536–44543, 2019.
DOI Google Scholar
[30]

W. Wang, H. Huang, L. Xue, Q. Li, R. Malekian, and Y. Zhang, Blockchain-assisted handover authentication for intelligent telehealth in multi-server edge computing environment, J. Syst. Architect., vol. 115, p. 102024, 2021.
DOI Google Scholar
[31]

S. A. ElRahman and A. S. Alluhaidan, Blockchain technology and IoT-edge framework for sharing healthcare services, Soft Comput., vol. 25, no. 21, pp. 13753–13777, 2021.
DOI Google Scholar
[32]
T. Hewa, A. Braeken, M. Ylianttila, and M. Liyanage, Multi-access edge computing and blockchain-based secure telehealth system connected with 5G and IoT, in Proc. GLOBECOM 2020–2020 IEEE Global Communications Conf., Taipei, China, 2020, pp. 1–6.
DOI
[33]
E. M. Abou-Nassar, A. M. Iliyasu, P. M. El-Kafrawy, O. Y. Song, A. K. Bashir, and A. A. A. El-Latif, DITrust chain: Towards blockchain-based trust models for sustainable healthcare IoT systems, IEEE Access, vol. 8, pp. 111223–111238, 2020.
DOI
[34]

J. Li, J. Cai, F. Khan, A. U. Rehman, V. Balasubramaniam, J. Sun, and P. Venu, A secured framework for SDN-based edge computing in IoT-enabled healthcare system, IEEE Access, vol. 8, pp. 135479–135490, 2020.
DOI Google Scholar
[35]
A. Azaria, A. Ekblaw, T. Vieira, and A. Lippman, MedRec: Using blockchain for medical data access and permission management, in Proc. 2016 2 nd Int. Conf. Open and Big Data (OBD), Vienna, Austria, 2016, pp. 25–30.
DOI
[36]

M. S. Christo, V. E. Jesi, U. Priyadarsini, V. Anbarasu, H. Venugopal, and M. Karuppiah, Ensuring improved security in medical data using ECC and blockchain technology with edge devices, Secur. Commun. Netw., vol. 2021, p. 6966206, 2021.
DOI Google Scholar
[37]

X. Li, X. Huang, C. Li, R. Yu, and L. Shu, EdgeCare: Leveraging edge computing for collaborative data management in mobile healthcare systems, IEEE Access, vol. 7, pp. 22011–22025, 2019.
DOI Google Scholar
[38]
H. Guo, W. Li, E. Meamari, C. C. Shen, and M. Nejad, Attribute-based multi-signature and encryption for EHR management: A blockchain-based solution, in Proc. IEEE Int. Conf. Blockchain and Cryptocurrency (ICBC), Toronto, Canada, 2020, pp. 1–5.
DOI
[39]
B. S. Egala, S. Priyanka, and A. K. Pradhan, SHPI: Smart healthcare system for patients in ICU using IoT, in Proc. IEEE Int. Conf. Advanced Networks and Telecommunications Systems (ANTS), Goa, India, 2019, pp. 1–6.
DOI
[40]
A. Al Omar, A. K. Jamil, M. S. H. Nur, M. M. Hasan, R. Bosri, M. Z. A. Bhuiyan, and M. S. Rahman, Towards a transparent and privacy-preserving healthcare platform with blockchain for smart cities, presented at the 2020 IEEE 19th Int. Conf. Trust, Security and Privacy in Computing and Communications (TrustCom), Guangzhou, China, 2020, pp. 1291–1296.
DOI
[41]

M. A. Jan, F. Khan, R. Khan, S. Mastorakis, V. G. Menon, M. Alazab, and P. Watters, Lightweight mutual authentication and privacy-preservation scheme for intelligent wearable devices in industrial-CPS, IEEE Trans. Ind. Inform., vol. 17, no. 8, pp. 5829–5839, 2021.
DOI Google Scholar
[42]
A. Islam and S. Y. Shin, BHMUS: Blockchain based secure outdoor health monitoring scheme using UAV in smart city, in Proc. 7 th Int. Conf. Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia, 2019, pp. 1–6.
DOI
[43]

R. Guo, H. Shi, Q. Zhao, and D. Zheng, Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records systems, IEEE Access, vol. 6, pp. 11676–11686, 2018.
DOI Google Scholar
[44]

J. A. Alzubi, Blockchain-based Lamport Merkle digital signature: Authentication tool in IoT healthcare, Comput. Commun., vol. 170, pp. 200–208, 2021.
DOI Google Scholar
[45]

Z. Ma, J. Ma, Y. Miao, X. Liu, K. K. R. Choo, R. Yang, and X. Wang, Lightweight privacy-preserving medical diagnosis in edge computing, IEEE Trans. Serv. Comput., vol. 15, no. 3, pp. 1606–1618, 2022.
DOI Google Scholar
[46]

P. K. Vadrevu, S. K. Adusumalli, and V. K. Mangalapalli, Personal privacy preserving data publication of COVID-19 pandemic data using edge computing, J. Crit. Rev., vol. 7, no. 19, pp. 8103–8111, 2020.
Google Scholar
[47]
Y. Guo, F. Liu, Z. Cai, L. Chen, and N. Xiao, FEEL: A federated edge learning system for efficient and privacy-preserving mobile healthcare, in Proc. 49 th Int. Conf. Parallel Processing, Edmonton, Canada, 2020, p. 9.
DOI
[48]

A. Alabdulatif, I. Khalil, X. Yi, and M. Guizani, Secure edge of things for smart healthcare surveillance framework, IEEE Access, vol. 7, p. 31010–31021, 2019.
DOI Google Scholar
[49]
R. Bosri, A. R. Uzzal, A. Al Omar, M. Z. A. Bhuiyan, and M. S. Rahman, HIDEchain: A user-centric secure edge computing architecture for healthcare IoT devices, in Proc. IEEE INFOCOM 2020-IEEE Conf. Computer Communications Workshops, Toronto, Canada, 2020, pp. 376–381.
DOI
[50]

F. Wang, L. Wang, G. Li, Y. Wang, C. Lv, and L. Qi, Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation, World Wide Web, vol. 25, no. 5, pp. 1809–1829, 2022.
DOI Google Scholar
[51]

X. Zhou, X. Xu, W. Liang, Z. Zeng, and Z. Yan, Deep-learning-enhanced multitarget detection for end-edge-cloud surveillance in smart IoT, IEEE Internet Things J., vol. 8, no. 16, pp. 12588–12596, 2021.
DOI Google Scholar
[52]
L. Qu, Y. Zhou, P. P. Liang, Y. Xia, F. Wang, E. Adeli, F.-F. Li, and D. Rubin, Rethinking architecture design for tackling data heterogeneity in federated learning, in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, New Orleans, LA, USA, 2022, pp. 10051–10061.
DOI
[53]

X. Zhou, X. Yang, J. Ma, and K. I. K. Wang, Energy-efficient smart routing based on link correlation mining for wireless edge computing in IoT, IEEE Internet Things J., vol. 9, no. 16, pp. 14988–14997, 2022.
DOI Google Scholar
[54]

I. A. Elgendy, A. Muthanna, M. Hammoudeh, H. Shaiba, D. Unal, and M. Khayyat, Advanced deep learning for resource allocation and security aware data offloading in industrial mobile edge computing, Big Data, vol. 9, no. 4, pp. 265–278, 2021.
DOI Google Scholar
[55]

M. Hammoudeh, G. Epiphaniou, S. Belguith, D. Unal, B. Adebisi, T. Baker, A. S. M. Kayes, and P. Watters, A service-oriented approach for sensing in the Internet of Things: Intelligent transportation systems and privacy use cases, IEEE Sens. J., vol. 21, no. 14, pp. 15753–15761, 2021.
DOI Google Scholar
[56]

M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, The case for VM-based cloudlets in mobile computing, IEEE Pervasive Comput., vol. 8, no. 4, pp. 14–23, 2009.
DOI Google Scholar
[57]

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, Edge computing: Vision and challenges, IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016.
DOI Google Scholar
[58]

S. Wang, Edge computing: Applications, state-of-the-art and challenges, Adv. Netw., vol. 7, no. 1, pp. 8–15, 2019.
DOI Google Scholar
[59]
S. B. Calo, M. Touna, D. C. Verma, and A. Cullen, Edge computing architecture for applying AI to IoT, in Proc. IEEE Int. Conf. Big Data (Big Data), Boston, MA, USA, 2017, pp. 3012–3016.
DOI
[60]
B. R. Behera and P. Suraj, Rectangular microstrip patch antenna for wireless fidelity application: Design of a Wi-Fi antenna using the concept of metamaterials, in Proc. IEEE Int. Conf. Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2016, pp. 1933–1937.
DOI
[61]

C. Bisdikian, An overview of the Bluetooth wireless technology, IEEE Commun. Mag., vol. 39, no. 12, pp. 86–94, 2001.
DOI Google Scholar
[62]
S. Safaric and K. Malaric, ZigBee wireless standard, in Proc. ELMAR 2006, Zadar, Croatia, 2006, pp. 259–262.
DOI
[63]

V. Coskun, B. Ozdenizci, and K. Ok, A survey on near field communication (NFC) technology, Wirel. Pers. Commun., vol. 71, no. 3, pp. 2259–2294, 2013.
DOI Google Scholar
[64]
B. Panchali, Edge computing-background and overview, in Proc. Int. Conf. Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018, pp. 580–582.
DOI
[65]

H. El-Sayed, S. Sankar, M. Prasad, D. Puthal, A. Gupta, M. Mohanty, C. T. Lin, Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment, IEEE Access, vol. 6, pp. 1706–1717, 2018.
DOI Google Scholar
[66]
OpenFog Consortium Architecture Working Group, OpenFog architecture overview, White Paper, https://site.ieee.org/denver-com/files/2017/06/OpenFog-Architecture-Overview-WP-2-2016.pdf, 2016.
[67]
K. Dolui and S. K. Datta, Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing, in Proc. Global Internet of Things Summit (GIoTS), Geneva, Switzerland, 2017, pp. 1–6.
DOI
[68]

X. Masip-Bruin, E. Marin-Tordera, A. Jukan, and G. J. Ren, Managing resources continuity from the edge to the cloud: Architecture and performance, Future Gener. Comput. Syst., vol. 79, pp. 777–785, 2018.
DOI Google Scholar
[69]

M. De Donno, K. Tange, and N. Dragoni, Foundations and evolution of modern computing paradigms: Cloud, IoT, edge, and fog, IEEE Access, vol. 7, pp. 150936–150948, 2019.
DOI Google Scholar
[70]
T. A. A. Alsbouí, M. Hammoudeh, Z. Bandar, and A. Nisbet, An overview and classification of approaches to information extraction in wireless sensor networks, in Proc. 5 th Int. Conf. Sensor Technologies and Applications & 1 st Int. Workshop on Sensor Networks for Supply Chain Management, Nice/Saint Laurent du Var, France, 2011, pp. 255–260.
[71]

M. Hammoudeh, R. Newman, C. Dennett, S. Mount, and O. Aldabbas, Map as a service: A framework for visualising and maximising information return from multi-modal wireless sensor networks, Sensors, vol. 15, no. 9, pp. 22970–23003, 2015.
DOI Google Scholar
[72]

W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, Edge computing: A survey, Future Gener. Comput. Syst., vol. 97, pp. 219–235, 2019.
DOI Google Scholar
[73]

A. Alrawais, A. Alhothaily, C. Hu, and X. Cheng, Fog computing for the internet of things: Security and privacy issues, IEEE Internet Comput., vol. 21, no. 2, pp. 34–42, 2017.
DOI Google Scholar
[74]

M. B. Mollah, M. A. K. Azad, and A. Vasilakos, Security and privacy challenges in mobile cloud computing: Survey and way ahead, J. Netw. Comput. Appl., vol. 84, pp. 38–54, 2017.
DOI Google Scholar
[75]

T. Bhatia and A. K. Verma, Data security in mobile cloud computing paradigm: A survey, taxonomy and open research issues, J. Supercomput., vol. 73, no. 6, pp. 2558–2631, 2017.
DOI Google Scholar
[76]

D. Boneh and M. Franklin, Identity-based encryption from the Weil pairing, SIAM J. Comput., vol. 32, no. 3, pp. 586–615, 2003.
DOI Google Scholar
[77]

S. Wang, J. Zhou, J. K. Liu, J. Yu, J. Chen, and W. Xie, An efficient file hierarchy attribute-based encryption scheme in cloud computing, IEEE Trans. Inform. Forensics Secur., vol. 11, no. 6, pp. 1265–1277, 2016.
DOI Google Scholar
[78]
S. Moffat, M. Hammoudeh, and R. Hegarty, A survey on ciphertext-policy attribute-based encryption (CP-ABE) approaches to data security on mobile devices and its application to IoT, in Proc. Int. Conf. Future Networks and Distributed Systems, Cambridge, UK, 2017, p. 34.
DOI
[79]

S. Belguith, N. Kaaniche, and M. Hammoudeh, Analysis of attribute-based cryptographic techniques and their application to protect cloud services, Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, p. e3667, 2022.
DOI Google Scholar
[80]

K. Liang, M. H. Au, J. K. Liu, W. Susilo, D. S. Wong, G. Yang, Y. Yu, and A. Yang, A secure and efficient ciphertext-policy attribute-based proxy re-encryption for cloud data sharing, Future Gener. Comput. Syst., vol. 52, pp. 95–108, 2015.
DOI Google Scholar
[81]
M. R. Baharon, Q. Shi, and D. Llewellyn-Jones, A new lightweight homomorphic encryption scheme for mobile cloud computing, in Proc. IEEE Int. Conf. Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, 2015, pp. 618–625.
DOI
[82]

D. Eckhoff and I. Wagner, Privacy in the smart city—applications, technologies, challenges, and solutions, IEEE Commun. Surv. Tutor., vol. 20, no. 1, pp. 489–516, 2018.
DOI Google Scholar
[83]

Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, Enabling public auditability and data dynamics for storage security in cloud computing, IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 5, pp. 847–859, 2011.
DOI Google Scholar
[84]

K. Yang and X. Jia, An efficient and secure dynamic auditing protocol for data storage in cloud computing, IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 9, pp. 1717–1726, 2013.
DOI Google Scholar
[85]

C. Lin, Z. Shen, Q. Chen, and F. T. Sheldon, A data integrity verification scheme in mobile cloud computing, J. Netw. Comput. Appl., vol. 77, pp. 146–151, 2017.
DOI Google Scholar
[86]

D. S. Touceda, J. M. S. Cámara, S. Zeadally, and M. Soriano, Attribute-based authorization for structured Peer-to-Peer (P2P) networks, Comput. Stand. Interfaces, vol. 42, pp. 71–83, 2015.
DOI Google Scholar
[87]

H. Liu, H. Ning, Q. Xiong, and L. T. Yang, Shared authority based privacy-preserving authentication protocol in cloud computing, IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 1, pp. 241–251, 2015.
DOI Google Scholar
[88]

X. Yang, X. Huang, and J. K. Liu, Efficient handover authentication with user anonymity and untraceability for mobile cloud computing, Future Gener. Comput. Syst., vol. 62, pp. 190–195, 2016.
DOI Google Scholar
[89]
M. Bahrami and M. Singhal, A light-weight permutation based method for data privacy in mobile cloud computing, in Proc. 3 rd IEEE Int. Conf. Mobile Cloud Computing, Services, and Engineering, San Francisco, CA, USA, 2015, pp. 189–198.
DOI
[90]

S. K. Pasupuleti, S. Ramalingam, and R. Buyya, An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing, J. Netw. Comput. Appl., vol. 64, pp. 12–22, 2016.
DOI Google Scholar
[91]
I. S. Park, Y. D. Lee, and J. Jeong, Improved identity management protocol for secure mobile cloud computing, in Proc. 46 th Hawaii Int. Conf. System Sciences, Wailea, HI, USA, 2013, pp. 4958–4965.
DOI
[92]

I. Khalil, A. Khreishah, and M. Azeem, Consolidated Identity Management System for secure mobile cloud computing, Comput. Netw., vol. 65, pp. 99–110, 2014.
DOI Google Scholar
[93]
M. Chen, W. Li, Z. Li, S. Lu, and D. Chen, Preserving location privacy based on distributed cache pushing, in Proc. IEEE Wireless Communications and Networking Conf. (WCNC), Istanbul, Turkey, 2014, pp. 3456–3461.
DOI
[94]
B. Niu, Q. Li, X. Zhu, G. Cao, and H. Li, Enhancing privacy through caching in location-based services, in Proc. IEEE Conf. Computer Communications (INFOCOM), Hong Kong, China, 2015, pp. 1017–1025.
DOI
[95]

N. H. Hassan and Z. Ismail, A conceptual model for investigating factors influencing information security culture in healthcare environment, Procedia Soc. Behav. Sci., vol. 65, pp. 1007–1012, 2012.
DOI Google Scholar
[96]

D. Box and D. Pottas, Improving information security behaviour in the healthcare context, Procedia Technol., vol. 9, pp. 1093–1103, 2013.
DOI Google Scholar
[97]

P. Kumar and H. J. Lee, Security issues in healthcare applications using wireless medical sensor networks: A survey, Sensors, vol. 12, no. 1, pp. 55–91, 2011.
DOI Google Scholar
[98]
D. Kotz, A threat taxonomy for mHealth privacy, in Proc. Third Int. Conf. Communication Systems and Networks (COMSNETS 2011), Bangalore, India, 2011, pp. 1–6.
DOI
[99]

I. Butun, N. Pereira, and M. Gidlund, Security risk analysis of LoRaWAN and future directions, Future Internet, vol. 11, no. 1, p. 3, 2018.
DOI Google Scholar
[100]

A. Singh and K. Chatterjee, Securing smart healthcare system with edge computing, Comput. Secur., vol. 108, p. 102353, 2021.
DOI Google Scholar
[101]

L. Qi, Y. Yang, X. Zhou, W. Rafique and J. Ma, Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0, IEEE Trans. Ind. Inform., vol. 18, no. 9, pp. 6503–6511, 2022.
DOI Google Scholar
[102]

X. Zhou, Y. Hu, J. Wu, W. Liang, J. Ma, and Q. Jin, Distribution bias aware collaborative generative adversarial network for imbalanced deep learning in industrial IoT, IEEE Trans. Ind. Inform., vol. 19, no. 1, pp. 570–580, 2023.
DOI Google Scholar
[103]
T. Javid, M. Faris, H. Beenish, and M. Fahad, Cybersecurity and data privacy in the cloudlet for preliminary healthcare big data analytics, in Proc. Int. Conf. Computing and Information Technology, Tabuk, Saudi Arabia, 2020, pp. 1–4.
DOI
[104]

P. Chaudhari and M. L. Das, Privacy preserving searchable encryption with fine-grained access control, IEEE Trans. Cloud Comput., vol. 9, no. 2, pp. 753–762, 2021.
DOI Google Scholar
[105]
T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785-794.
DOI
[106]

L. Minder and A. Sinclair, The extended k-tree algorithm, J. Cryptol., vol. 25, no. 2, pp. 349–382, 2012.
DOI Google Scholar
[107]
L. Sweeney, k-anonymity: A model for protecting privacy, Int. J. Unc. Fuzz. Knowl.-Based Syst., vol. 10, no. 5, pp. 557–570, 2002.
DOI
[108]
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Trans. Knowl. Discov. Data, vol. 1, no. 1, p. 3–es, 2007.
DOI
[109]
C. Dwork, Differential privacy: A survey of results, in Proc. 5 th Int. Conf. Theory and Applications of Models of Computation, Xi’an, China, 2008, pp. 1–19.
DOI
[110]
S. Vishnu, S. R. J. Ramson, and R. Jegan, Internet of medical things (IoMT)-An overview, in Proc. 5 th Int. Conf. Devices, Circuits and Systems (ICDCS), Coimbatore, India, 2020, pp. 101–104.
DOI
[111]
M. Steichen, B. Fiz, R. Norvill, W. Shbair, and R. State, Blockchain-based, decentralized access control for IPFS, in Proc. IEEE Int. Conf. Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Halifax, NS, Canada, 2018, pp. 1499–1506.
DOI
[112]

R. C. Wasserman, Electronic medical records (EMRs), epidemiology, and epistemology: Reflections on EMRs and future pediatric clinical research, Acad. Pediatr., vol. 11, no. 4, pp. 280–287, 2011.
DOI Google Scholar
[113]

M. S. Hossain, G. Muhammad, and N. Guizani, Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics, IEEE Netw., vol. 34, no. 4, pp. 126–132, 2020.
DOI Google Scholar
[114]
ResNet-50 convolutional neural network-MATLAB resnet50-mathworks.com, https://www.mathworks.com/help/deeplearning/ref/resnet50.html, 2023.
[115]
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 2818–2826.
DOI
[116]

T. T. Huong, T. P. Bac, D. M. Long, B. D. Thang, N. T. Binh, T. D. Luong and T. K. Phuc, Lockedge: Low-complexity cyberattack detection in IoT edge computing, IEEE Access, vol. 9, pp. 29696–29710, 2021.
DOI Google Scholar
[117]
D. Kim, J. Mun, Y. Park, J. Choi, and J. Choi, Planning system architecture of fat-client management for customized healthcare services in edge computing environment, in Proc. 2020 5 th Int. Conf. Intelligent Information Technology, Hanoi, Vietnam, 2020, pp. 91–96.
DOI
[118]

M. Mistry, Softwarization of the infrastructure of Internet of Things for secure and smart healthcare, Ann. Rom. Soc. Cell Biol., vol. 25, no. 6, pp. 6680–6701, 2021.
Google Scholar
[119]
D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, Blockchain and edge computing for decentralized EMRs sharing in federated healthcare, in Proc GLOBECOM IEEE Global Communications Conf., Taipei, China, 2020, pp. 1–6.
DOI
[120]
M. Campagna, SEC 4: Elliptic curve Qu-Vanstone implicit certificate scheme (ECQV), Standards for Efficient Cryptography, https://www.secg.org/sec4-1.0.pdf, 2013.
[121]
H. Guo, W. Li, M. Nejad, and C. C. Shen, Access control for electronic health records with hybrid blockchain-edge architecture, in Proc. IEEE Int. Conf. Blockchain (Blockchain), Atlanta, GA, USA, 2019, pp. 44–51.
DOI
[122]
J. Fan and F. Vercauteren, Somewhat Practical Fully Homomorphic Encryption. Cryptology ePrint Archive, https://eprint.iacr.org/2012/144, 2012.
[123]
A. Frank, UCI machine learning repository, http://archive.ics.uci.edu/ml, 2010.
[124]
D. Hankerson, A. Menezes, and S. Vanstone, Guide to Elliptic Curve Cryptography. New York, NY, USA: Springer, 2004.
[125]

M. A. Jan, W. Zhang, M. Usman, Z. Tan, F. Khan, and E. Luo, SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application, J. Netw. Comput. Appl., vol. 137, pp. 1–10, 2019.
DOI Google Scholar
[126]

S. R. Eddy, Hidden Markov models, Curr. Opin. Struct. Biol., vol. 6, no. 3, pp. 361–365, 1996.
DOI Google Scholar
[127]
X. Liu, J. Ma, Q. Li, J. Xiong, and F. Huang, Attribute based multi-signature scheme in the standard model, in Proc. Ninth Int. Conf. Computational Intelligence and Security, Emeishan, China, 2013, pp. 738–742.
DOI
[128]

D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, BEdgeHealth: A decentralized architecture for edge-based IoMT networks using blockchain, IEEE Internet Things J., vol. 8, no. 14, pp. 11743–11757, 2021.
DOI Google Scholar
[129]

K. Kritikos, B. Pernici, P. Plebani, C. Cappiello, M. Comuzzi, S. Benrernou, I. Brandic, A. Kertész, M. Parkin, and M. Carro, A survey on service quality description, ACM Comput. Surv., vol. 46, no. 1, p. 1, 2013.
DOI Google Scholar
[130]
R. Baskar, R. Dhanagopal, K. Elangovan, and K. Gunasekaran, VLSI based architecture in ECG monitoring for adaptive power management in wireless bio signal acquisition network, Journal of Physics : Conference Series, vol. 1964, no. 6, p. 062090, 2021.
DOI
[131]

Q. Yang, Q. Liu, and H. Lv, A decentralized system for medical data management via blockchain, J. Internet Technol., vol. 21, no. 5, pp. 1335–1345, 2020.
Google Scholar
[132]

Q. Xia, E. B. Sifah, K. O. Asamoah, J. Gao, X. Du, and M. Guizani, MeDShare: Trust-less medical data sharing among cloud service providers via blockchain, IEEE Access, vol. 5, pp. 14757–14767, 2017.
DOI Google Scholar
[133]

B. D. Deebak, F. Al-Turjman, and L. Mostarda, Seamless secure anonymous authentication for cloud-based mobile edge computing, Comput. Electr. Eng., vol. 87, p. 106782, 2020.
DOI Google Scholar
[134]

Z. Ali, M. S. Hossain, G. Muhammad, I. Ullah, H. Abachi, and A. Alamri, Edge-centric multimodal authentication system using encrypted biometric templates, Future Gener. Comput. Syst., vol. 85, pp. 76–87, 2018.
DOI Google Scholar
[135]

L. Witt, M. Heyer, K. Toyoda, W. Samek, and D. Li, Decentral and incentivized federated learning frameworks: A systematic literature review, IEEE Internet Things J., vol. 10, no. 4, pp. 3642–3663, 2023.
DOI Google Scholar
[136]
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, Communication-efficient learning of deep networks from decentralized data, in Proc. 20 th Int. Conf. Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.
[137]

M. Ali, F. Naeem, M. Tariq, and G. Kaddoum, Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey, IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 778–789, 2023.
DOI Google Scholar
[138]

K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, and J. Kalpathy-Cramer, Distributed deep learning networks among institutions for medical imaging, J. Am. Med. Inform. Assoc., vol. 25, no. 8, pp. 945–954, 2018.
DOI Google Scholar
[139]
M. G. Poirot, P. Vepakomma, K. Chang, J. Kalpathy-Cramer, R. Gupta, and R. Raskar, Split learning for collaborative deep learning in healthcare, arXiv preprint arXiv: 1912.12115, 2019.
[140]
Y. Zhang, R. Jia, H. Pei, W. Wang, B. Li, and D. Song, The secret revealer: Generative model-inversion attacks against deep neural networks, in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 250–258.
DOI
[141]
R. Tomsett, K. Chan, and S. Chakraborty, Model poisoning attacks against distributed machine learning systems, in Proc. Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, MD, USA, 2019, pp. 481–489.
DOI
[142]
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, Deep learning with differential privacy, in Proc. 2016 ACM SIGSAC Conf. Computer and Communications Security, Vienna, Austria, 2016, pp. 308–318.
DOI
[143]

Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao, FedHealth: A federated transfer learning framework for wearable healthcare, IEEE Intell. Syst., vol. 35, no. 4, pp. 83–93, 2020.
DOI Google Scholar
[144]

G. Carvalho, B. Cabral, V. Pereira, and J. Bernardino, Edge computing: Current trends, research challenges and future directions, Computing, vol. 103, no. 5, pp. 993–1023, 2021.
DOI Google Scholar
[145]
R. Dave, N. Seliya, and N. Siddiqui, The benefits of edge computing in healthcare, smart cities, and IoT, arXiv preprint arXiv: 2112.01250, 2021.
[146]

A. Pekar, J. Mocnej, W. K. G. Seah, and I. Zolotova, Application domain-based overview of IoT network traffic characteristics, ACM Comput. Surv., vol. 53, no. 4, p. 87, 2020.
DOI Google Scholar
[147]
M. Fredrikson, S. Jha, and T. Ristenpart, Model inversion attacks that exploit confidence information and basic countermeasures, in Proc. 22 nd ACM SIGSAC Conf. Computer and Communications Security, Denver, CO, USA, 2015, pp. 1322–1333.
DOI
[148]
L. Kong, G. Li, W. Rafique, S. Shen, Q. He, M. R. Khosravi, R. Wang, and L. Qi, Time-aware missing healthcare data prediction based on ARIMA model, IEEE/ACM Trans. Comput. Biol. Bioinform.
[149]
J. Saleem, B. Adebisi, R. Ande, and M. Hammoudeh, A state of the art survey-Impact of cyber attacks on SME’s, in Proc. Int. Conf. Future Networks and Distributed Systems, Cambridge, UK, 2017, p. 52.
DOI
[150]

S. Khanagha, S. Ansari, S. Paroutis, and L. Oviedo, Mutualism and the dynamics of new platform creation: A study of Cisco and fog computing, Strateg. Manag. J., vol. 43, no. 3, pp. 476–506, 2022.
DOI Google Scholar
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Received: 30 May 2023
Revised: 30 June 2023
Accepted: 27 July 2023
Published: 09 February 2024
Issue date: August 2024

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