Skip to main content

Advertisement

SpringerLink
Log in

IFHDS: Intelligent Framework for Securing Healthcare BigData

  • Transactional Processing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

A Correction to this article was published on 22 April 2019

This article has been updated

Abstract

Big data has become one of the most imperative technologies for collecting, handling and analysing enormous volumes of data in a high-performance environment. Enterprise healthcare organizations needs high compute power for the large volume of sensitive data, as well as large storage for storing both data and results, preferably in the cloud. However, security and privacy of patient data have become a critical issue that restricts many healthcare services from using cloud services to their optimal level. Therefore, this issue has limited healthcare organizations from migrating patient data to a cloud storage, because the cloud operators have chance to access sensitive data without the owner’s permission. This paper proposes an intelligent security system called Intelligent Framework for Healthcare Data Security (IFHDS). IFHDS enables to secure and process large-scale data using column-based approach with less impact on the performance of data processing. The intelligent framework intends masking personal data and to encrypt sensitive data only. The proposed IFHDS splits sensitive data into multiple parts according to sensitivity level, where each part is stored separately over distributed cloud storage. Splitting data based on sensitivity level prevents cloud provider to break complete record of data if succeeds to decrypt part of data. The experimental results confirm that the proposed system secure the sensitive patient data with an acceptable computation time compared to recent security approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

  • 22 April 2019

    The original version of this article unfortunately contained a mistake. All algorithms are missing in the online PDF version. The original version has been corrected.

References

  1. Jiang, P., Winkley, J., Zhao, C., Munnoch, R., Min, G., and Yang, L. T., An intelligent information forwarder for healthcare big data systems with distributed wearable sensors. IEEE Syst. J. 10(3):1147–1159, 2016.

    Article  Google Scholar 

  2. Wang, Y., Kung, L., and Byrd, T.A., Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 2016.

  3. Kohli, R., and Tan, S. S.-L., Electronic health records: how can IS researchers contribute to transforming healthcare? Mis Quart. 40(3):553–573, 2016.

    Article  Google Scholar 

  4. Poppe, K., Wolfert, S., Verdouw, C., and Renwick, A European perspective on the economics of big data. Farm Policy J. 12(1):11–19, 2015.

    Google Scholar 

  5. Wu, X., Zhu, X., Wu, G. Q., and Ding, Data mining with big data. IEEE Trans Knowl. Data Eng. 26(1):97–107, 2014.

    Article  Google Scholar 

  6. Abbas, and Khan, A review on the state-of-the-art privacy-preserving approaches in the e-health clouds. IEEE J. Biomed. Health Inform. 18(4):1431–1441, 2014.

    Article  Google Scholar 

  7. Singh, G., A study of encryption algorithms (RSA, DES, 3DES and AES) for information security. Int. J. Comput. Applic. 67, no 19, 2013.

    Article  Google Scholar 

  8. Li, Y., Gai, K., Qiu, L., Qiu, M., and Zhao, H., Intelligent cryptography approach for secure distributed big data storage in cloud computing. Inform. Sci. 387:103–115, 2017.

    Article  Google Scholar 

  9. Essa, Y. M., Attiya, G., and El-Sayed, A., Mobile agent based new framework for improving big data analysis. IEEE international conference on cloud computing and big data (CloudCom-Asia 2013), FuZhou, China, 2013.

  10. Pussewalage, G., Harsha, S., and Oleshchuk, V.A., A distributed multi-authority attribute based encryption scheme for secure sharing of personal health records. Proc 22nd ACM Sym Access Control Models Technol: 255–262. ACM, 2017.

  11. Raghupathi, W., and Viju, Big data analytics in healthcare: promise and potential. Health Inform. Sci. Syst. 2(1):3, 2014.

    Article  Google Scholar 

  12. Mathew, PrabhaSusy, and Anitha S. Pillai. “Big data solutions in healthcare: problems and perspectives. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 1–6, 2015.

  13. Priyanka, K., and NagarathnaKulennavar. “A survey on big data analytics in health care. Int. J. Comput. Sci. Inform. Technol., vol 5, no. 4, pp. 5865–5868, 2014.

  14. Dobre, C., and Xhafa, F., Parallel programming paradigms and frameworks in big data era. Int J Parallel Program. 42(5):710–738, 2014.

    Article  Google Scholar 

  15. Xhafa, F., Li, J., Zhao, G., Li, J., Chen, X., and Wong, D. S., Designing cloud-based electronic health record system with attribute-based encryption. Multimed Tools Applic 74(10):3441–3458, 2015.

    Article  Google Scholar 

  16. Zaharia, M., Xin, R. S. et al., Apache Spark: a unified engine for big data processing. Commun. ACM 59(11):56–65, 2016.

    Article  Google Scholar 

  17. Liu, J., Huang, X., and Liu, J. K., Secure sharing of personal health records in cloud computing: ciphertext-policy attribute-based signcryption. Fut. Gen. Comput. Syst. 52:67–76, 2015.

    Article  Google Scholar 

  18. Barbierato, E., Gribaudo, M., and Iacono, M., Performance evaluation of NoSQL big-data applications using multi-formalism models. Future Generation Computer Systems 37:345–353, 2014.

    Article  Google Scholar 

  19. Cash, D., Jaeger, J. et al., Dynamic searchable encryption in very-large databases: data structures and implementation. NDSS 14:23–26, 2014.

    Google Scholar 

  20. Nambiar R., Bhardwaj, R., Sethi, A. and Vargheese., A look at challenges and opportunities of big data analytics in healthcare. 2013 IEEE International Conference on Big Data, IEEE. 17–22, 2013.

  21. Asri, H., Mousannif, H., Al Moatassime, H. and Noel., Big data in healthcare: Challenges and opportunities. 2015 International Conference on Cloud Technologies and Applications (CloudTech), IEEE: 1–7, 2015.

  22. Kepner, J., Gadepally, V., and et al., Computing on masked data: a high performance method for improving big data veracity. 2014 IEEE In High Performance Extreme Computing Conference (HPEC). 1–6., 2014.

  23. Dong, X., Li, R. et al., Secure sensitive data sharing on a big data platform. Tsinghua Sci. Technol. 20(1):72–80, 2015.

    Article  Google Scholar 

  24. Morse, R. E., Nadkarni, P., Schoenfeld, D. A., and Finkelstein, D. M., Web-browser encryption of personal health information. BMC Medical Informatics and Decision Making, 2011.

  25. Wicks, P., and Little, M. The virtuous circle of the quantified self: a human computational approach to improved health outcomes. Handbook of human computation, pp. 105–129. Springer New York, 2013.

  26. Rahmani, G., Negash, A., and et al., Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach. Accepted in Future Generation Computer Systems, 2017.

  27. Rao, S., Suma, S. N., and Sunitha, M., Security solutions for big data analytics in healthcare 2015 Second International Conference on Advances Computing and Communication Engineering (ICACCE). 510–514. IEEE, 2015.

  28. Hossain, M. S., Muhammad, G. et al., Toward end-to-end biometrics-based security for IoT infrastructure. IEEE Wireless Commun. 23(5):44–51, 2016.

    Article  CAS  Google Scholar 

  29. Hänsel, K., Wilde, N., Haddadi, H., and Alomainy, A.., Challenges with current wearable technology in monitoring health data and providing positive behavioural support. Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare. 158–161, 2015.

  30. Essa, Y. M., Attiya, G., and El-Sayed, A., New framework for improving big data analysis using Mobile agent. Int. J. Adv. Comput. Sci. Applic. (IJACSA) 05(03):25–32, 2014.

    Google Scholar 

  31. Zhang, Y., and Liu, D., Improving the efficiency of storing for small files in hdfs. 2012 International Conference on Computer Science & Service System (CSSS). 2239–2242. IEEE, 2012.

  32. Aravinth, M. S., Shanmugapriyaa, M. S., Sowmya, M. S., and Arun, An efficient HADOOP frameworks SQOOP and ambari for big data processing. Int. J. Innov. Res. Sci. Technol. 1(10):252–255, 2015.

    Google Scholar 

  33. Patil, P., Narayankar, P., Narayan, D. G., and Meena, S. M., A comprehensive evaluation of cryptographic algorithms: DES, 3DES, AES, RSA and Blowfish. Procedia Computer Science 78:617–624, 2016.

    Article  Google Scholar 

  34. K. Gai, M. Qiu, H. Zhao, “Security-aware efficient mass distributed storage approach for cloud systems in big data”, IEEE international conference on intelligent data and security (IDS), IEEE, New York, USA. 140–145, 2016.

  35. Zhang, Y., Qiu, M., Tsai, C.-W., Hassan, M. M., and Alamri, A., Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1):88–95, 2017.

    Article  CAS  Google Scholar 

  36. Essa, Y. M., El-Mahalawy, A., Attiya, G., and El-Sayed, A.., A distributed multiagent architecture for self-healing healthcare data center. 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS 2017), AMA International university Bahrain, 2017.

  37. Ali, M., Khan, S., and Vasilakos, A., Security in cloud computing: Opportunities and challenges. Inf. Sci. 305:357–383, 2015.

    Article  Google Scholar 

  38. Gai, K., Qiu, M., Zhao, H., Xiong, J., Privacy-aware adaptive data encryption strategy of big data in cloud computing”, 3rd IEEE International Conference on Cyber Security and Cloud Computing. 273–278, China, 2016.

  39. Yu, H., Patnaik, S., Ji, S., Jia, L., and Yang, T., Design and Implementation of Multi-Agent Online Auction Systems in Cloud Computing. Int. J. Enterprise Inform. Syst. (IJEIS) 13(1):50–66, 2017.

    Article  Google Scholar 

  40. Mohit, P., Amin, R., Karati, A., Biswas, G. P., and Khan, M. K., A standard mutual authentication protocol for cloud computing based health care system. Journal of medical systems, vol.41, no.4, 2017.

  41. Park, D., Kang, K., Hong, J., and Cho, Y., An efficient Hadoop data replication method design for heterogeneous clusters. Proceedings of the 31st Annual ACM Symposium on Applied Computing. 2182–2184. ACM, 2016.

  42. Islam, M. R., Habiba, N. M., Agent based framework for providing security to data storage in cloud. Proceedings of 15th International Conference on Computer and Information Technology (ICCIT), IEEE, 2012.

  43. Islam, M. R., Habiba, M., Kashem, M. I. I., A framework for providing security to Personal Healthcare Records. International Conference on Networking, Systems and Security (NSysS), IEEE, 2017.

  44. Yang, W., Wang, S., Hu, J., Zheng, G., Chaudhry, J., Adi, E., and Valli, C., Securing mobile healthcare data: A smart card based cancelable Finger-Vein Bio-Cryptosystem. IEEE Access 6:36939–36947, 2018.

    Article  Google Scholar 

  45. Chattaraj, D., Sarma, M., and Samanta, D., Stochastic petri net based modeling for analyzing dependability of big data storage system. Emerging Technologies in Data Mining and Information Security, pp. 473–484. Springer, Singapore, 2019.

    Google Scholar 

  46. Chattaraj, D., Sarma, M., Das, A. K., Kumar, N., Rodrigues, J. J. P. C., and Park, Y., HEAP: An Efficient and Fault-Tolerant Authentication and Key Exchange Protocol for Hadoop-Assisted Big Data Platform. IEEE Access 6:75342–75382, 2018.

    Article  Google Scholar 

  47. Gai, K., and Qiu, M., Blend arithmetic operations on tensor-based fully homomorphic encryption over real numbers. IEEE Trans. Indust. Inform. 14(8):3590–3598, 2018.

    Article  Google Scholar 

  48. Hospital Compare datasets, Centers for Medicare & Medicaid Services, https://data.medicare.gov/data/hospital-compare, 2018.

  49. National Toxicology Program, Chemical Effects in Biological Systems (CEBS) database, 2018.

  50. Li, W., Yang, Y., and Dong Y. ,A novel cost-effective dynamic data replication strategy for reliability in cloud data centres. 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing. 496–502. IEEE, 2011.

Download references

Author information

Authors and Affiliations

  1. Senior Big Data Engineer, Idealo, Berlin, Germany

    Youssef M. Essa

  2. Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt

    Ezz El-Din Hemdan, Ahmed El-Mahalawy, Gamal Attiya & Ayman El-Sayed

Authors
  1. Youssef M. Essa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ezz El-Din Hemdan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed El-Mahalawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gamal Attiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ayman El-Sayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youssef M. Essa.

Ethics declarations

The authors declare that they have no of interest, there is no funding source, and this article does not contain any studies with human participants or animals performed by any of the authors. The authors whose names are listed conflict immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: all algorithms in PDF are missing.

This article is part of the Topical Collection on Transactional Processing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Essa, Y.M., Hemdan, E.ED., El-Mahalawy, A. et al. IFHDS: Intelligent Framework for Securing Healthcare BigData. J Med Syst 43, 124 (2019). https://doi.org/10.1007/s10916-019-1250-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1250-4

Keywords

Search

Navigation