Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 3/2023

11-03-2023

A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data

Authors: Jayanti Dansana, Manas Ranjan Kabat, Prasant Kumar Pattnaik

Published in: Wireless Personal Communications | Issue 3/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.
previous article Concurring of Neural Machines for Robust Session Key Generation and Validation in Telecare Health System During COVID-19 Pandemic
next article AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Literature
1.
Rajan, J. P., Rajan, S. E., Martis, R. J., & Panigrahi, B. K. (2020). Fog computing employed computer aided cancer classification system using deep neural network in internet of things based healthcare system. Journal of Medical Systems, 44(2), 1–10.
2.
Biswal, A. K., Maiti, P., Bebarta, S., Sahoo, B., & Turuk, A. K. (2020). Authenticating IoT devices with blockchain. Advanced Applications of Blockchain Technology, pp. 177–205.
3.
Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Future Generation Computer Systems, 104(1), 187–200.
4.
Narayanan, U., Paul, V., & Joseph, S. (2022). A novel system architecture for secure authentication and data sharing in cloud enabled Big Data Environment. Journal of King Saud University-Computer and Information Sciences, 34(6), 3121–3135.
5.
Kaur, P., Kumar, R., & Kumar, M. (2019). A healthcare monitoring system using random forest and internet of things (IoT). Multimedia Tools and Applications, 78(14), 19905–19916.
6.
Rathi, M., & Rajavat, A. (2020, April). High Dimensional Data Processing in Privacy Preserving Data Mining. In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 212–217). IEEE.
7.
Saini, R., Kumar, P., Kaur, B., Roy, P. P., Dogra, D. P., & Santosh, K. C. (2019). Kinect sensor-based interaction monitoring system using the BLSTM neural network in healthcare. International Journal of Machine Learning and Cybernetics, 10(9), 2529–2540.
8.
Ghosh, A., Raha, A., & Mukherjee, A. (2020). Energy-efficient IoT-health monitoring system using approximate computing. Internet of Things, 9(1), 100166.
9.
Hao, Y., Usama, M., Yang, J., Hossain, M. S., & Ghoneim, A. (2019). Recurrent convolutional neural network based multimodal disease risk prediction. Future Generation Computer Systems, 92(1), 76–83.
10.
Malathi, D., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., & Sangaiah, A. K. (2019). Hybrid reasoning-based privacy-aware disease prediction support system. Computers & Electrical Engineering, 73(1), 114–127.
11.
Gouse, S. M., & Mohan, G. K. (2022). A hybrid data perturbation and mean clustering approach based privacy preserving classification model for large databases. International Journal of System Assurance Engineering and Management, pp. 1–11.
12.
Vijayashree, J., & Parveen Sultana, H. (2020). Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network. Health and Technology, 10(1), 207–216.
13.
Xu, Y., Bhuiyan, M. Z. A., Wang, T., Zhou, X., & Singh, A. K. (2022). C-fdrl: Context-aware privacy-preserving offloading through federated deep reinforcement learning in cloud-enabled IoT. IEEE Transactions on Industrial Informatics, 19(2), 1155–1164.
14.
Lavanya, R., & Karpagam, M. (2020). Enhancing the security of AES through small scale confusion operations for data communication. Microprocessors and Microsystems, 75(1), 103041.
15.
Gangireddy, V. K. R., Kannan, S., & Subburathinam, K. (2021). Implementation of enhanced blowfish algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3999–4005.
16.
Aghapour, S., Kaveh, M., Martín, D., & Mosavi, M. R. (2020). An ultra-lightweight and provably secure broadcast authentication protocol for smart grid communications. IEEE Access, 8, 125477–125487.
17.
Muthu, B., Sivaparthipan, C. B., Manogaran, G., Sundarasekar, R., Kadry, S., Shanthini, A., & Dasel, A. (2020). IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-peer networking and applications, 13(6), 2123–2134.
18.
Bhattacharya, S., Maddikunta, P. K. R., Hakak, S., Khan, W. Z., Bashir, A. K., Jolfaei, A., & Tariq, U. (2020). Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset. Multimedia Tools and Applications, 81(1), 41429–41453.
19.
Tzirakis, P., Chen, J., Zafeiriou, S., & Schuller, B. (2021). End-to-end multimodal affect recognition in real-world environments. Information Fusion, 68, 46–53.
20.
Aitzaouiat, C. E., Latif, A., Benslimane, A., & Chin, H. H. (2022). Machine learning based prediction and modeling in healthcare secured internet of things. Mobile Networks and Applications, 27(1), 84–95.
21.
Jamal, A. A., Majid, A. A. M., Konev, A., Kosachenko, T., & Shelupanov, A. (2021). A review on security analysis of cyber physical systems using Machine learning. Materials Today: Proceedings.
22.
Suthar, M. S., Zimmerman, M. G., Kauffman, R. C., Mantus, G., Linderman, S. L., Hudson, W. H., & Wrammert, J. (2020). Rapid generation of neutralizing antibody responses in COVID-19 patients. Cell Reports Medicine, 1(3), 1000.
23.
Li, D., Han, D., Weng, T. H., Zheng, Z., Li, H., Liu, H., & Li, K. C. (2022). Blockchain for federated learning toward secure distributed machine learning systems: A systemic survey. Soft Computing, 26(9), 4423–4440.
24.
Mani, N., Singh, A., & Nimmagadda, S. L. (2020). An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Procedia Computer Science, 167, 850–859.
25.
AlZubi, A. A., Al-Maitah, M., & Alarifi, A. (2021). Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques. Soft Computing, 25(18), 12319–12332.
26.
Sharma, P., Jain, S., Gupta, S., & Chamola, V. (2021). Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. Ad Hoc Networks, 123(1), 102685.
27.
Maniriho, P., Mahmood, A. N., & Chowdhury, M. J. M. (2022). A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges. Future Generation Computer Systems, 130, 1–18.
28.
Thakkar, A., & Lohiya, R. (2021). A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges. Archives of Computational Methods in Engineering, 28(4), 3211–3243.
29.
Sharma, S., Alam, A. M., & Chen, K. (2021). Image disguising for protecting data and model confidentiality in outsourced deep learning. In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) (pp. 71–77). IEEE.
30.
Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., & Li, X. (2021). A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Networks and Applications, 26(1), 234–252.
31.
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., ... & Philip, S. Y. (2022). Privacy and robustness in federated learning: Attacks and defenses. IEEE transactions on neural networks and learning systems.
32.
Unal, D., Hammoudeh, M., Khan, M. A., Abuarqoub, A., Epiphaniou, G., & Hamila, R. (2021). Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things. Computers & Security, 109(1), 102393.
33.
Mohiyuddin, A., Javed, A. R., Chakraborty, C., Rizwan, M., Shabbir, M., & Nebhen, J. (2022). Secure cloud storage for medical IoT data using adaptive neuro-fuzzy inference system. International Journal of Fuzzy Systems, 24(2), 1203–1215.
34.
Rashid, M., Singh, H., Goyal, V., Parah, S. A., & Wani, A. R. (2021). Big data based hybrid machine learning model for improving performance of medical Internet of Things data in healthcare systems. In Healthcare Paradigms in the Internet of Things Ecosystem (pp. 47–62). Academic Press.
35.
Gadekallu, T. R., Manoj, M. K., Kumar, N., Hakak, S., & Bhattacharya, S. (2021). Blockchain-based attack detection on machine learning algorithms for IoT-Based e-health applications. IEEE Internet of Things Magazine, 4(3), 30–33.
36.
Babar, M., Tariq, M. U., & Jan, M. A. (2020). Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid. Sustainable Cities and Society, 62(1), 102370.
37.
Golec, M., Ozturac, R., Pooranian, Z., Gill, S. S., & Buyya, R. (2021). iFaaSBus: A security and privacy based lightweight framework for serverless computing using IoT and machine learning. IEEE Transactions on Industrial Informatics., 18(5), 3522–3529.
38.
Masud, M., Gaba, G. S., Alqahtani, S., Muhammad, G., Gupta, B. B., Kumar, P., & Ghoneim, A. (2020). A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care. IEEE Internet of Things Journal, 8(21), 15694–15703.
39.
Alowibdi, J. S., Alshdadi, A. A., Daud, A., Dessouky, M. M., & Alhazmi, E. A. (2021). Coronavirus pandemic (COVID-19): Emotional toll analysis on Twitter. International Journal on Semantic Web and Information Systems (IJSWIS), 17(2), 1–21.
40.
Rahman, M. A., Hossain, M. S., Alrajeh, N. A., & Gupta, B. B. (2021). A multimodal, multimedia point-of-care deep learning framework for COVID-19 diagnosis. ACM Transactions on Multimidia Computing Communications and Applications, 17(1s), 1–24.
41.
Sedik, A., Hammad, M., Abd El-Samie, F. E., Gupta, B. B., & Abd El-Latif, A. A. (2021). Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Computing and Applications, pp. 1–18.
42.
Chopra, M., Singh, S. K., Gupta, A., Aggarwal, K., Gupta, B. B., & Colace, F. (2022). Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic. Sustainable Technology and Entrepreneurship, 1(2), 100012.
43.
Pashchenko, D. (2021). fully remote software development due to COVID factor: Results of industry research (2020). International Journal of Software Science and Computational Intelligence (IJSSCI), 13(3), 64–70.
44.
Zhou, Z., Gaurav, A., Gupta, B. B., Hamdi, H., & Nedjah, N. (2021). A statistical approach to secure health care services from DDoS attacks during COVID-19 pandemic. Neural Computing and Applications, pp. 1–14.
45.
Tewari, A., & Gupta, B. B. (2020). Secure timestamp-based mutual authentication protocol for IoT devices using RFID tags. International Journal on Semantic Web and Information Systems (IJSWIS), 16(3), 20–34.
46.
Bulla, C. M., & Birje, M. N. (2021). A multi-agent-based data collection and aggregation model for fog-enabled cloud monitoring. International Journal of Cloud Applications and Computing (IJCAC), 11(1), 73–92.
47.
Adat, V., & Gupta, B. B. (2018). Security in internet of things: Issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67, 423–441.
48.
Zhang, J., Wang, Z., Wang, D., Zhang, X., Gupta, B. B., Liu, X., & Ma, J. (2021). A secure decentralized spatial crowdsourcing scheme for 6G-enabled network in box. IEEE Transactions on Industrial Informatics, 18(9), 6160–6170.
49.
Horák, K., Bošanský, B., Tomášek, P., Kiekintveld, C., & Kamhoua, C. (2019). Optimizing honeypot strategies against dynamic lateral movement using partially observable stochastic games. Computers & Security, 87(1), 101579.
50.
Qiao, J. F., Lu, C., & Li, W. J. (2018). Design of dynamic modular neural network based on adaptive particle swarm optimization algorithm. IEEE Access, 6(1), 10850–10857.
51.
Chamikara, M. A. P., Bertók, P., Liu, D., Camtepe, S., & Khalil, I. (2020). Efficient privacy preservation of big data for accurate data mining. Information Sciences, 527(1), 420–443.MathSciNetMATH
52.
Hosseini, N., Fakhar, F., Kiani, B., & Eslami, S. (2019). Enhancing the security of patients’ portals and websites by detecting malicious web crawlers using machine learning techniques. International journal of medical informatics, 132(1), 103976.
53.
Akhbarifar, S., Javadi, H. H. S., Rahmani, A. M., & Hosseinzadeh, M. (2020). A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment. Personal and Ubiquitous Computing, pp. 1–17.
54.
Malarvizhi Kumar, P., Hong, C. S., Chandra Babu, G., Selvaraj, J., & Gandhi, U. D. (2021). Cloud-and IoT-based deep learning technique-incorporated secured health monitoring system for dead diseases. Soft Computing, 25(18), 12159–12174.
Metadata
Title
A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data
Authors
Jayanti Dansana
Manas Ranjan Kabat
Prasant Kumar Pattnaik
Publication date
11-03-2023
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2023
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-023-10363-x

Other articles of this Issue 3/2023

Wireless Personal Communications 3/2023 Go to the issue

OriginalPaper

A New Energy-Aware Method for Balancing the Load on Wireless IoT Devices Using an Optimization Algorithm Based on Chaos Theory

OriginalPaper

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

OriginalPaper

Achieving Energy Efficiency and Impact of SAR in a WBAN Through Optimal Placement of the Relay Node

OriginalPaper

A Lightweight and Robust Stream Cipher Based on PI for Intelligent Transportation Systems

OriginalPaper

Fuzzy Based Image Forgery Classification with SWT-DCT-LBP Based Hybrid Features

OriginalPaper

An Improvement of DV-Hop Localization Algorithm Based on Improved Adaptive Genetic Algorithm for Wireless Sensor Networks