Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
An Overview of Medical Internet of Things, Artificial Intelligence, and Cloud Computing Employed in Health Care from a Modern Panorama Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

6. Privacy and Security Concerns in IoT-Based Healthcare Systems

verfasst von : Joseph Bamidele Awotunde, Rasheed Gbenga Jimoh, Sakinat Oluwabukonla Folorunso, Emmanuel Abidemi Adeniyi, Kazeem Moses Abiodun, Oluwatobi Oluwaseyi Banjo

Erschienen in: The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The Internet of Things (IoT) Healthcare system has become one of the most indispensable parts of human lives, and this has dramatically increased the medical information system that brings about big data. Healthcare practitioners are already adopting wearable devices based on the IoT to streamline the diagnosis, monitoring, prediction, and treatment process. The affordability and user-friendliness of the usage of IoT start revolutionizing healthcare services. Billions of sensors, devices, and vehicles in recent times have been connected through the Internet using IoT technologies. For continuous health tracking, wireless sensor networks can be implemented to enhance the well-being of patients, make the healthcare system more effective, and help respond quickly to emergencies. The IoT-based healthcare can be employed to enhance the well-being of patients, make the healthcare system more effective, and help respond quickly to emergencies. However, these systems also pose significant risks in terms of privacy and security issues surrounding data transfer, processing, monitoring, and documentation. Such healthcare data protection, security, and privacy concerns may result from a delay in the progress of treatment, diagnosis, and even endangering the life of the patient. Therefore, this chapter presents a summary of the IoT, comprising its architecture, and reveals the IoT-based healthcare application privacy and security concerns. The chapter also proposed a framework to secure healthcare information in the IoT environment. This will help to protect healthcare data on the IoT platform and meet the strict security and privacy needs of ubiquitous medical requests.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Smart IoT Treatment: Making Medical Care More Intelligent
Nächstes Kapitel IoT Healthcare Applications
Literatur
1.
Firouzi, F., Rahmani, A. M., Mankodiya, K., Badaroglu, M., Merrett, G. V., Wong, P., & Farahani, B. (2018). Internet-of-things and big data for smarter healthcare: From device to architecture, applications, and analytics.
2.
Medaglia, C. M., & Serbanati, A. (2010). An overview of privacy and security issues on the internet of things. In The internet of things (pp. 389–395). Springer.CrossRef
3.
Yang, P., Wu, W., Moniri, M., & Chibelushi, C. C. (2012). Efficient object localization using sparsely distributed passive RFID tags. IEEE Transactions on Industrial Electronics, 60(12), 5914–5924.CrossRef
4.
Adeniyi, E. A., Ogundokun, R. O., & Awotunde, J. B. (2021). IoMT-based wearable body sensors network healthcare monitoring system. In IoT in healthcare and ambient assisted living (pp. 103–121). Springer.CrossRef
5.
Riggins, F. J., & Wamba, S. F. (2015, January). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In 2015 48th Hawaii international conference on system sciences (pp. 1531–1540). IEEE.
6.
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to the future. International Journal of Information Management, 36(6), 1231–1247.CrossRef
7.
Pramanik, P. K. D., Upadhyaya, B. K., Pal, S., & Pal, T. (2019). Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare. In Healthcare data analytics and management (pp. 1–58). Academic.
8.
Darwish, A., Ismail Sayed, G., & Ella Hassanien, A. (2019). The impact of implantable sensors in biomedical technology on the future of healthcare systems. Intelligent pervasive computing systems for smarter healthcare (pp. 67–89).
9.
Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of medical things (IOMT): Applications, benefits, and future challenges in the healthcare domain. The Journal of Communication, 12(4), 240–247.
10.
Manogaran, G., Chilamkurti, N., & Hsu, C. H. (2018). Emerging trends, issues, and challenges on the internet of medical things and wireless networks. Personal and Ubiquitous Computing, 22(5–6), 879–882.CrossRef
11.
Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020). The future of healthcare internet of things: A survey of emerging technologies. IEEE Communications Surveys and Tutorials, 22(2), 1121–1167.CrossRef
12.
Akhtar, P., Khan, Z., Rao-Nicholson, R., & Zhang, M. (2019). Building relationship innovation in global collaborative partnerships: Big data analytics and traditional organizational powers. R&D Management, 49(1), 7–20.CrossRef
13.
Chan, M. M., Plata, R. B., Medina, J. A., Alario-Hoyos, C., Rizzardini, R. H., & de la Roca, M. (2018). Analysis of behavioral intention to use cloudbased tools in a MOOC: A technology acceptance model approach. Journal of Universal Computer Science, 24(8), 1072–1089.
14.
Zhang, P., Zhou, M., & Fortino, G. (2018). Security and trust issues in fog computing: A survey. Future Generation Computer Systems, 88, 16–27.CrossRef
15.
Karati, A., Islam, S. H., Biswas, G. P., Bhuiyan, M. Z. A., Vijayakumar, P., & Karuppiah, M. (2017). Provably secure identity-based signcryption scheme for the crowdsourced industrial internet of things environments. IEEE Internet of Things Journal, 5(4), 2904–2914.CrossRef
16.
Solomon, M., & Elias, E. P. (2018). Privacy protection for wireless medical sensor data. International Journal of Scientific Research in Science and Technology, 4, 1439–1440.
17.
Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2017). Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of Things Journal, 5(4), 2483–2495.CrossRef
18.
Shen, J., Gui, Z., Ji, S., Shen, J., Tan, H., & Tang, Y. (2018). Cloud-aided lightweight certificates authentication protocol with anonymity for wireless body area networks. Journal of Network and Computer Applications, 106, 117–123.CrossRef
19.
Roy, S., Chatterjee, S., Das, A. K., Chattopadhyay, S., Kumari, S., & Jo, M. (2017). Chaotic map-based anonymous user authentication scheme with user biometrics and fuzzy extractor for crowdsourcing internet of things. IEEE Internet of Things Journal, 5(4), 2884–2895.CrossRef
20.
Luo, E., Liu, Q., & Wang, G. (2016). Hierarchical multi-authority and attribute-based encryption friend discovery scheme in mobile social networks. IEEE Communications Letters, 20(9), 1772–1775.CrossRef
21.
ur Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial internet of things. Future Generation Computer Systems, 99, 247–259.CrossRef
22.
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2018). An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481–489.CrossRef
23.
Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: State of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10–16.CrossRef
24.
Pace, P., Aloi, G., Caliciuri, G., Gravina, R., Savaglio, C., Fortino, G., … & Mortara, M. (2019, April). Inter-health: An interoperable IoT solution for active and assisted living healthcare services. In 2019 IEEE 5th world forum on internet of things (WF-IoT) (pp. 81–86). IEEE.
25.
Veltink, P. H., Bussmann, H. J., De Vries, W., Martens, W. J., & Van Lummel, R. C. (1996). Detection of static and dynamic activities using uniaxial accelerometers. IEEE Transactions on Rehabilitation Engineering, 4(4), 375–385.CrossRef
26.
Lyons, G. M., Culhane, K. M., Hilton, D., Grace, P. A., & Lyons, D. (2005). A description of an accelerometer-based mobility monitoring technique. Medical Engineering & Physics, 27(6), 497–504.CrossRef
27.
Luštrek, M., & Kaluža, B. (2009). Fall detection and activity recognition with machine learning. Informatica, 33(2).
28.
Pappas, I. P., Popovic, M. R., Keller, T., Dietz, V., & Morari, M. (2001). A reliable gait phase-detection system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9(2), 113–125.CrossRef
29.
Paganelli, F., & Giuli, D. (2007, May). An ontology-based context model for home health monitoring and alerting in chronic patient care networks. In 21st international conference on advanced information networking and applications workshops (AINAW'07) (Vol. 2, pp. 838–845). IEEE.
30.
Salarian, A., Russmann, H., Wider, C., Burkhard, P. R., Vingerhoets, F. J., & Aminian, K. (2007). Quantification of tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system. IEEE Transactions on Biomedical Engineering, 54(2), 313–322.CrossRef
31.
Moncada-Torres, A., Leuenberger, K., Gonzenbach, R., Luft, A., & Gassert, R. (2014). Activity classification is based on inertial and barometric pressure sensors at different anatomical locations. Physiological Measurement, 35(7), 1245.CrossRef
32.
Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., & Lovell, N. H. (2010). Barometric pressure and triaxial accelerometry-based fall event detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 619–627.CrossRef
33.
Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2019). The impact of the hybrid platform of the internet of things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing, 10(10), 4151–4166.CrossRef
34.
Mukherjee, P., & Mukherjee, A. (2019). Advanced processing techniques and secure architecture for sensor networks in ubiquitous healthcare systems. In Sensors for health monitoring (pp. 3–29). Academic.CrossRef
35.
Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2020). CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access, 8, 52541–52549.CrossRef
36.
Wang, N., Deng, Z., Wen, L. M., Ding, Y., & He, G. (2019). Understanding the use of smartphone apps for health information among pregnant Chinese women: Mixed methods study. JMIR mHealth and uHealth, 7(6), e12631.CrossRef
37.
Le Kernec, J., Fioranelli, F., Ding, C., Zhao, H., Sun, L., Hong, H., … Romain, O. (2019). Radar signal processing for sensing in assisted living: The challenges associated with the real-time implementation of emerging algorithms. IEEE Signal Processing Magazine, 36(4), 29–41.CrossRef
38.
Farahani, B., Firouzi, F., & Chakrabarty, K. (2020). Healthcare IoT. In Intelligent internet of things (pp. 515–545). Springer.CrossRef
39.
Padmavathi, S. (2020). A fog-based approach for real-time analytics of IoT-enabled healthcare. In The internet of things use cases for the healthcare industry (pp. 43–66). Springer.
40.
Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., & Schreier, G. (2010, April). The internet of things for ambient assisted living. In 2010 seventh international conference on information technology: new generations (pp. 804–809). IEEE.
41.
Brownsell, S., & Hawley, M. (2004). Fall detectors: Do they work or reduce the fear of falling? Housing, Care, and Support, 7(1), 18.CrossRef
42.
Wagner, F., Basran, J., & Dal Bello-Haas, V. (2012). A review of monitoring technology for use with older adults. Journal of Geriatric Physical Therapy, 35(1), 28–34.CrossRef
43.
Nguyen, H. H., Mirza, F., Naeem, M. A., & Nguyen, M. (2017, April). A review of IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback. In 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD) (pp. 257–262). IEEE.
44.
Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., & Muzammil, H. (2018). Based on real-time remote health monitoring systems: A review on patients' prioritization and related “big data” using body sensors information and communication technology. Journal of Medical Systems, 42(2), 30.CrossRef
45.
Ayo, F. E., Ogundokun, R. O., Awotunde, J. B., Adebiyi, M. O., & Adeniyi, A. E. (2020, July). Severe acne skin disease: A fuzzy-based method for diagnosis. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12254 LNCS (pp. 320–334).
46.
Azimi, I., Rahmani, A. M., Liljeberg, P., & Tenhunen, H. (2017). Internet of things for remote elderly monitoring: A study from a user-centered perspective. Journal of Ambient Intelligence and Humanized Computing, 8(2), 273–289.CrossRef
47.
Fortino, G., Gravina, R., & Galzarano, S. (2018). Wearable computing: From modeling to implementation of wearable systems based on body sensor networks. John Wiley & Sons.CrossRef
48.
Zheng, M., Liu, P. X., Gravina, R., & Fortino, G. (2018). An emerging wearable world: New gadgetry produces a rising tide of changes and challenges. IEEE Systems, Man, and Cybernetics Magazine, 4(4), 6–14.CrossRef
49.
Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., & Jafari, R. (2012). Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Transactions on Human Machine Systems, 43(1), 115–133.CrossRef
50.
Fortino, G., Guerrieri, A., Bellifemine, F., & Giannantonio, R. (2009, October). Platform-independent development of collaborative wireless body sensor network applications: SPINE2. In 2009 IEEE international conference on systems, man and cybernetics (pp. 3144–3150). IEEE.
51.
Lee, S. Y., Chou, C. L., Hsu, S. P., Shih, C. C., Yeh, C. C., Hung, C. J., … Liao, C. C. (2016). Outcomes after stroke in patients with previous pressure ulcer: A Nationwide matched retrospective cohort study. Journal of Stroke and Cerebrovascular Diseases, 25(1), 220–227.CrossRef
52.
Jaul, E., & Menzel, J. (2014). Pressure ulcers in the elderly, as a public health problem. Journal of General Practice, 2, 5.
53.
Pal, D., Triyason, T., & Funikul, S. (2017, December). Smart homes and quality of life for the elderly: A systematic review. In 2017 IEEE international symposium on multimedia (ISM) (pp. 413–419). IEEE.
54.
Tun, S. Y. Y., Madanian, S., & Mirza, F. (2020). Internet of things (IoT) applications for elderly care: A reflective review. Aging Clinical and Experimental Research, 1–13.
55.
Shany, T., Redmond, S. J., Marschollek, M., & Lovell, N. H. (2012). Assessing fall risk using wearable sensors: A practical discussion. Zeitschrift für Gerontologie und Geriatrie, 45(8), 694–706.CrossRef
56.
Sharma, C. A., Kumar, A. K. S. V., Prasad, A., Begum, R., Sharvani, G. S., & Manjunath, A. E. (2018, January). Multifaceted bio-medical applications of exoskeleton: A review. In 2018 2nd international conference on inventive systems and control (ICISC) (pp. 11–15). IEEE.
57.
Oladele, T. O., Ogundokun, R. O., Awotunde, J. B., Adebiyi, M. O., & Adeniyi, J. K. (2020, July). Diagmal: A malaria coactive neuro-fuzzy expert system. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 2020, 12254 LNCS (pp. 428–441).
58.
Yein, N., & Pal, S. (2017, January). Technological assistance for falls among the aging population: A review. In International conference on research into design (pp. 409–419). Springer.
59.
De Bruin, E. D., Hartmann, A., Uebelhart, D., Murer, K., & Zijlstra, W. (2008). Wearable systems for monitoring mobility-related activities in older people: A systematic review. Clinical Rehabilitation, 22(10–11), 878–895.CrossRef
60.
Amaraweera, S. P., & Halgamuge, M. N. (2019). Internet of things in the healthcare sector: Overview of security and privacy issues. In Security, privacy, and trust in the IoT environment (pp. 153–179). Springer.CrossRef
61.
Tao, H., Bhuiyan, M. Z. A., Abdalla, A. N., Hassan, M. M., Zain, J. M., & Hayajneh, T. (2018). Secured data collection with hardware-based ciphers for IoT-based healthcare. IEEE Internet of Things Journal, 6(1), 410–420.CrossRef
62.
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2018). Continuous patient monitoring with a patient-centric agent: A block architecture. IEEE Access, 6, 32700–32726.CrossRef
63.
Vermesan, O., & Friess, P. (Eds.). (2014). Internet of things-from research and innovation to market deployment (Vol. 29). River Publishers.
64.
Malan, D. J., Fulford-Jones, T., Welsh, M., & Moulton, S. (2004). Codeblue: An ad hoc sensor network infrastructure for emergency medical care. In International workshop on wearable and implantable body sensor networks.
65.
Kumar, P., & Lee, H. J. (2012). Security issues in healthcare applications using wireless medical sensor networks: A survey. Sensors, 12(1), 55–91.CrossRef
66.
Abdulraheem, M., Awotunde J. B., Jimoh, R. G., & Oladipo, I. D. (2021). An efficient lightweight crypotographic Algorithm for IoT security. Communication in Computer and Information Science, 1350, 444–456.
67.
Mutlag, A. A., Ghani, M. K. A., Arunkumar, N. A., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems, 90, 62–78.CrossRef
68.
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Security and privacy in the medical internet of things: A review. Security and Communication Networks, 2018.
69.
Tang, J., Liu, A., Zhao, M., & Wang, T. (2018). An aggregate signature based trust routing for data gathering in sensor networks. Security and Communication Networks, 2018.
70.
Sun, W., Cai, Z., Liu, F., Fang, S., & Wang, G. (2017, October). A survey of data mining technology on electronic medical records. In 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom) (pp. 1–6). IEEE.
71.
Thomas, C. (2020). Introductory chapter: Computer security threats. In Computer security threats. IntechOpen.CrossRef
72.
Jones, J. C. (2016). Nurses rank as most honest, ethical profession for 14th straight year.
73.
Burhan, M., Rehman, R. A., Khan, B., & Kim, B. S. (2018). IoT elements, layered architectures, and security issues: A comprehensive survey. Sensors, 18(9), 2796.CrossRef
74.
Alassaf, N., Gutub, A., Parah, S. A., & Al Ghamdi, M. (2019). Enhancing speed of SIMON: A light-weight-cryptographic algorithm for IoT applications. Multimedia Tools and Applications, 78(23), 32633–32657.CrossRef
75.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: A survey. Journal of Big Data, 2(1), 1–32.CrossRef
76.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247–5261.CrossRef
77.
Fortino, G., Guerrieri, A., Bellifemine, F. L., & Giannantonio, R. (2009, July). SPINE2: Developing BSN applications on heterogeneous sensor nodes. In 2009 IEEE international symposium on industrial embedded systems (pp. 128–131). IEEE.
78.
Iyengar, S., Bonda, F. T., Gravina, R., Guerrieri, A., Fortino, G., & Sangiovanni-Vincentelli, A. (2008, March). A framework for creating healthcare monitoring applications using wireless body sensor networks. In proceedings of the ICST 3rd international conference on body area networks (pp. 1–2).
79.
Fortino, G., Galzarano, S., Gravina, R., & Li, W. (2015). A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Information Fusion, 22, 50–70.CrossRef
80.
Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., & Savaglio, C. (2016, April). A mobile multi-technology gateway to enable IoT interoperability. In 2016 IEEE first international conference on internet-of things design and implementation (IoTDI) (pp. 259–264). IEEE.
81.
Dedeoglu, V., Jurdak, R., Dorri, A., Lunardi, R. C., Michelin, R. A., Zorzo, A. F., & Kanhere, S. S. (2020). Blockchain technologies for IoT. In Advanced applications of blockchain technology (pp. 55–89). Springer.CrossRef
82.
Suresh, A., Nandagopal, M., Raj, P., Neeba, E. A., & Lin, J. W. (2020). Industrial IoT application architectures and use cases. CRC Press.CrossRef
83.
Glaroudis, D., Iossifides, A., & Chatzimisios, P. (2020). The survey, comparison, and research challenges of IoT application protocols for smart farming. Computer Networks, 168, 107037.CrossRef
84.
Nie, X., & Zhong, X. (2013, March). Security in the internet of things based on RFID: Issues and current countermeasures. In Proceedings of the 2nd international conference on computer science and electronics engineering. Atlantis Press.
85.
Fathy, M., Samouti, S. A., Samouti, A., & Senior, I. T. (2016). NFC threats and attacks: Applying a low cost algorithm for secure channel using Twofish. Bulletin de la Société Royale des Sciences de Liège, 85, 1475–1486.CrossRef
86.
Sfar, A. R., Natalizio, E., Challal, Y., & Chtourou, Z. (2018). A roadmap for security challenges in the internet of things. Digital Communications and Networks, 4(2), 118–137.CrossRef
87.
Bayat, M., Beheshti-Atashgah, M., Barari, M., & Aref, M. R. (2019). Cryptanalysis and improvement of a user authentication scheme for the internet of things using elliptic curve cryptography. IJ Network Security, 21(6), 897–911.
88.
Puthal, D., Yang, L. T., Dustdar, S., Wen, Z., Jun, S., Moorsel, A. V., & Ranjan, R. (2020). A user-centric security solution for the internet of things and edge convergence. ACM Transactions on Cyber-Physical Systems, 4(3), 1–19.CrossRef
89.
Anand, S., & Sharma, A. (2020). Assessment of security threats on IoT based applications. Materials Today: Proceedings.
90.
Ayo, F. E., Folorunso, S. O., Abayomi-Alli, A. A., Adekunle, A. O., & Awotunde, J. B. (2020). Network intrusion detection is based on deep learning model optimized with rule-based hybrid feature selection. Information Security Journal: A Global Perspective, 29, 1–17.
91.
Abikoye, O. C., Ojo, U. A., Awotunde, J. B., & Ogundokun, R. O. (2020). A safe and secured iris template using steganography and cryptography. Multimedia Tools and Applications, 79(31–32), 23483–23506.CrossRef
92.
Banerjee, S., Hemphill, T., & Longstreet, P. (2018). Wearable devices and healthcare: Data sharing and privacy. The Information Society, 34(1), 49–57.CrossRef
93.
Nelson, G. S. (2015). Practical implications of sharing data: A primer on data privacy, anonymization, and de-identification. In SAS global forum proceedings (pp. 1–23).
94.
Portability, I. (2012). Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (HIPAA) privacy rule.
95.
Hall, R., Rinaldo, A., & Wasserman, L. (2013). Differential privacy for functions and functional data. Journal of Machine Learning Research, 14(Feb), 703–727.MathSciNetMATH
96.
Gupta, S., Kumar, P., Singh, J. P., & Singh, M. P. (2016). Privacy preservation of source location using phantom nodes. In Information technology: New generations (pp. 247–256). Springer.CrossRef
97.
Yao, L., Kang, L., Shang, P., & Wu, G. (2013). Protecting the sink location privacy in wireless sensor networks. Personal and Ubiquitous Computing, 17(5), 883–893.CrossRef
98.
Manjula, R., & Datta, R. (2018). A novel source location privacy preservation technique to achieve enhanced privacy and network lifetime in WSNs. Pervasive and Mobile Computing, 44, 58–73.CrossRef
99.
Huang, M., Liu, A., Wang, T., & Huang, C. (2018). Green data gathering under delay differentiated services constraint for the internet of things. Wireless Communications and Mobile Computing, 2018.
Metadaten
Titel
Privacy and Security Concerns in IoT-Based Healthcare Systems
verfasst von
Joseph Bamidele Awotunde
Rasheed Gbenga Jimoh
Sakinat Oluwabukonla Folorunso
Emmanuel Abidemi Adeniyi
Kazeem Moses Abiodun
Oluwatobi Oluwaseyi Banjo
Copyright-Jahr
2021
Buch
The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Print ISBN: 978-3-030-75219-4

Electronic ISBN: 978-3-030-75220-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-75220-0_6

Neuer Inhalt

    Bildnachweise