Skip to main content
Springer Nature Link
Log in

Hybrid Data Privacy and Anonymization Algorithms for Smart Health Applications

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In line with the concept of Smart cities and Smart World, there have been bewildering developments in the field of Internet of Things (IoT) and Big Data Analytics where one most noteworthy territory is that of Smart Health. Private Health data are required to be transmitted over wireless communication systems where the main problem arising is that of data security and privacy. Various algorithms have been implemented to achieve data privacy. In this work, k-anonymity, differential privacy, k-map, \(\ell\) -diversity and a hybrid of the three algorithms combined with ℓ-diversity have been analyzed using algorithms running on a raspberry pi (edge) for a breast cancer dataset. The results demonstrated a difference in using stand-alone algorithms in terms of output anonymized dataset, execution time, information loss, risk analysis, and statistical analysis. If solely based on its execution time, the hybrids took less than 20 s in comparison to the stand-alone taking nearly 90 s, leading to conclusion that hybrid algorithms are much more suitable in terms of efficiency. Having the option to share anonymized information quicker will reduce financial and admin costs indicating that hybrids should be the main focus even if all algorithms fulfilled their objective of achieving data privacy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Active Advice (2018) "What is Smart Health and How do People Benefit? | ActiveAdvice," 28 March 2018. [Online]. Available: https://www.activeadvice.eu/news/concept-projects/what-is-smart-health-and-how-do-people-benefit/. Accessed 3 Dec 2018.

  2. Mandel JC, Kreda DA, Mandl KD, Kohane I, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc. 2016;23(5):899–908.

    Article  Google Scholar 

  3. Suzuki T, Tanaka H, Minami S, Yamada H, Miyata T (2013) Wearable wireless vital monitoring technology for smart health care. In: International Symposium on medical information and communication technology (ISMICT), Tokyo, Japan, 2013

  4. Yang L, Qiao Y, Liu Z, Ma J, Li X. Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm. Soft Comput. 2018;22(2):453–64.

    Article  Google Scholar 

  5. Zhang Z, Li C, Gupta BB, Niu D. Efficient compressed Ciphertext length scheme using multi-authority CP-ABE. IEEE Access. 2018;6:38273–84.

    Article  Google Scholar 

  6. Pavlich-Mariscal JA, Demurjian SA, Michel LD. A framework of composable access control features: preserving. Comput Secur. 2010;29(3):350–79.

    Article  Google Scholar 

  7. Xiao Z, Fu X, Goh RSM. Data privacy-preserving automation architecture for industrial data exchange in smart cities. IEEE Trans Industr Inf. 2018;14(6):2780–91.

    Article  Google Scholar 

  8. Amin R, Islam SKH, Gope P, Choo KKR, Tapas N. Anonymity preserving and lightweight multimedical server authentication protocol for telecare medical information system. IEEE J Biomed Health Inf. 2019;23(4):1749–59.

    Article  Google Scholar 

  9. Uddin M, Islam S, Al-Nemrat A. A dynamic access control model using authorising workflow and task-role-based access control. IEEE Access. 2019;7:166676–89.

    Article  Google Scholar 

  10. Zhang Y, Lang P, Zheng D, Yang M, Guo R. A secure and privacy-aware smart health system with secret key leakage resilience. Secur Commun Netw. 2018;2018:1–13.

    Google Scholar 

  11. Liu H, Yao X, Yang T, Ning H. Cooperative privacy preservation for wearable devices in hybrid computing based smart health. IEEE Internet of Things J. 2018;6(2):1352–62.

    Article  Google Scholar 

  12. Chaudhary R, Jindal A, Aujla GS, Kumar N, Das AK, Saxena N. LSCSH: lattice-based secure cryptosystem for smart healthcare in smart cities environment. Inst Electric Electron Eng (IEEE). 2018;56(4):24–32.

    Google Scholar 

  13. Sweeney L. k-Anonimity: A model for protecting privacy. Int J Uncertain Puzziness Knowl-Based Syst. 2002;10(5):557–70.

    Article  MathSciNet  Google Scholar 

  14. Dwork C. Differential data privacy. In: Bugliesi M, Preneel B, Sassone V, Wegener I, editors. Automata, languages and programming, vol. 4052., Lecture Notes in Computer ScienceBerlin: Springer; 2006. p. 1–12.

    Chapter  Google Scholar 

  15. S. Tu. An introduction to differential privacy," 2013. [Online]. https://people.eecs.berkeley.edu/~stephentu/writeups/6885-lec20-b.pdf. Accessed 3 December 2018.

  16. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertai Fuzziness Knowl-Based Syst. 2002;10(5):571–88.

    Article  MathSciNet  Google Scholar 

  17. Gkoulalas-Divanis A, Loukides G. Medical data privacy handbook. Springer; 2015.

    Book  Google Scholar 

  18. Bradley M. k-Unlinkability: A privacy protection model for distributed data. Data Knowl Eng. 2008;64(1):294–311.

    Article  Google Scholar 

  19. El Emam K, Dankar FK. Protecting privacy using k-anonymity. J Am Med Inf Assoc. 2008;15(5):627–37.

    Article  Google Scholar 

  20. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. L-diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data (TKDD). 2007;1(1):1–12.

    Article  Google Scholar 

  21. Ohrn A, Ohno-Machado L. Using Boolean reasoning to anonymize databases. Artif Intell Med. 1999;15(3):235–54.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the University of Mauritius for the necessary facilities provided towards the proper conduction of this work.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Mauritius, Réduit, Mauritius

    Y. N. Fakeeroodeen & Y. Beeharry

Authors
  1. Y. N. Fakeeroodeen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Y. Beeharry
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Y. Beeharry.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fakeeroodeen, Y.N., Beeharry, Y. Hybrid Data Privacy and Anonymization Algorithms for Smart Health Applications. SN COMPUT. SCI. 2, 126 (2021). https://doi.org/10.1007/s42979-021-00547-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00547-2

Keywords