[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Improvement of medical data security using SABES optimization algorithm

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This paper proposes a bit-level cryptographic model for encrypting medical signals, patient information, clinical data, and images of various modalities. The model has utilized chaotic maps due to properties like non-periodicity, nonlinearity, ergodicity, deterministic, unpredictable, and sensitive to the initial conditions. Three chaotic maps are employed in a chained sequence at different levels of the model to elevate the randomness and provide uniform key distribution with larger key space. Also, the self-adaptive bald eagle search optimization algorithm has been employed to enhance the properties of chaotic maps by optimizing the initialization and control parameters. The cyclic redundancy check approach is involved to detect any change in the data during the transmission at the receiver side along with circular shift to make encrypted data resilient of differential attack. Therefore, the encryption model boosts the unpredictability, security, and protection against a wide range of threats. The encryption model performance efficiency is demonstrated using different types of security analysis to show resistance against statistical, entropy, differential, cropping, and brute-force attacks.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability statement

The data that support the findings of the study are available in Kaggle (https://www.kaggle.com/), National Institution of Health (NIH) websites (https://openi.nlm.nih.gov/gridquery?it=xg), University Medical Center Groningen (UMCG) website (http://www.cs.rug.nl/~imaging/databases/melanoma_naevi/), and Cancer imaging archive (https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM).

References

  1. Abanda Y, Tiedeu A (2016) Image encryption by chaos mixing. IET Image Proc 10(10):742–750. https://doi.org/10.1049/iet-ipr.2015.0244

    Article  Google Scholar 

  2. Abdelfatah RI (2020) Audio encryption scheme using self-adaptive bit scrambling and two multi chaotic-based dynamic DNA computations. IEEE Access 8:69894–69907. https://doi.org/10.1109/ACCESS.2020.2987197

    Article  Google Scholar 

  3. Abokhdair NO, Manaf ABA, Zamani M (2010) Integration of chaotic map and confusion technique for color medical image encryption. In: 6th International Conference on Digital Content, Multimedia Technology and Its Applications, IDC2010. IEEE, pp 20–23. https://ieeexplore.ieee.org/document/5568578

  4. Alawida M (2023) A novel chaos-based permutation for image encryption. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2023.101595

    Article  Google Scholar 

  5. Algarni AD, Soliman NF, Abdallah HA et al (2021) Encryption of ECG signals for telemedicine applications. Multimed Tools Appl 80(7):10679–10703. https://doi.org/10.1007/s11042-020-09369-5

    Article  Google Scholar 

  6. Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53(3):2237–2264. https://doi.org/10.1007/s10462-019-09732-5

    Article  Google Scholar 

  7. Amina S, Mohamed FK (2018) An efficient and secure chaotic cipher algorithm for image content preservation. Commun Nonlinear Sci Numer Simul 60:12–32. https://doi.org/10.1016/j.cnsns.2017.12.017

    Article  Google Scholar 

  8. Ashtiyani M, Birgani PM, Hosseini HM (2008) Chaos-based medical image encryption using symmetric cryptography. In: 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications. IEEE, pp 1–5. https://doi.org/10.1109/ICTTA.2008.4530291

  9. Banu SA, Amirtharajan R (2020) A robust medical image encryption in dual domain: chaos-DNA-IWT combined approach. Med Biol Eng Comput 58(7):1445–1458. https://doi.org/10.1007/s11517-020-02178-w

    Article  Google Scholar 

  10. Chai X, Zhang J, Gan Z et al (2019) Medical image encryption algorithm based on Latin square and memristive chaotic system. Multimed Tools Appl 78(24):35419–35453. https://doi.org/10.1007/s11042-019-08168-x

    Article  Google Scholar 

  11. Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871. https://doi.org/10.1016/j.cor.2004.08.012

    Article  Google Scholar 

  12. Chen X, Hu CJ (2017) Adaptive medical image encryption algorithm based on multiple chaotic mapping. Saudi J Biol Sci 24(8):1821–1827. https://doi.org/10.1016/j.sjbs.2017.11.023

    Article  Google Scholar 

  13. Chen Y, Tang C, Yi Z (2020) A novel image encryption scheme based on PWLCM and standard map. Complexity 2020:1–23. https://doi.org/10.1155/2020/3026972

    Article  Google Scholar 

  14. Cheng G, Wang C, Chen H (2019) A novel color image encryption algorithm based on hyperchaotic system and permutation-diffusion architecture. Int J Bifurc Chaos 29(09):1950115. https://doi.org/10.1142/S0218127419501153

    Article  MathSciNet  Google Scholar 

  15. Eberhart R, Yuhui Shi (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol 1. IEEE, pp 94–100. https://doi.org/10.1109/CEC.2001.934376

  16. Galiero R, Pafundi PC, Nevola R et al (2020) The importance of telemedicine during COVID-19 pandemic: A focus on diabetic retinopathy. J Diabetes Res 2020:1–8. https://doi.org/10.1155/2020/9036847

    Article  Google Scholar 

  17. Gao X, Mou J, Xiong L et al (2022) A fast and efficient multiple images encryption based on single-channel encryption and chaotic system. Nonlinear Dyn 108(1):613–636. https://doi.org/10.1007/s11071-021-07192-7

    Article  Google Scholar 

  18. Hua Z, Jin F, Xu B et al (2018) 2D logistic-sine-coupling map for image encryption. Signal Process 149:148–161. https://doi.org/10.1016/j.sigpro.2018.03.010

    Article  Google Scholar 

  19. Iatropoulos A, Moysis L, Giakoumis A, et al (2021) Medical data encryption based on a modified sinusoidal 1d chaotic map and its microcontroller implementation. In: 10th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, Thessaloniki, Greece, 1, pp 1–4. https://doi.org/10.1109/MOCAST52088.2021.9493422

  20. Iqbal N, Hanif M, Rehman ZU et al (2022) On the novel image encryption based on chaotic system and DNA computing. Multimed Tools Appl 81(6):8107–8137. https://doi.org/10.1007/s11042-022-11912-5

    Article  Google Scholar 

  21. Lawnik M, Berezowski M (2022) New Chaotic system: M-map and its application in Chaos-based cryptography. Symmetry 14(5):895. https://doi.org/10.3390/sym14050895. https://www.mdpi.com/2073-8994/14/5/895

  22. Li C, Luo G, Qin K et al (2017) An image encryption scheme based on chaotic tent map. Nonlinear Dyn 87(1):127–133. https://doi.org/10.1007/s11071-016-3030-8

    Article  Google Scholar 

  23. Lian S, Sun J, Wang Z (2005) Security analysis of a chaos-based image encryption algorithm. Phys A 351(2–4):645–661. https://doi.org/10.1016/j.physa.2005.01.001

    Article  Google Scholar 

  24. Lin CF (2016) Chaotic visual cryptosystem using empirical mode decomposition algorithm for clinical EEG signals. J Med Syst 40(3):1–10. https://doi.org/10.1007/s10916-015-0414-0

    Article  Google Scholar 

  25. Lin CF, Chung CH (2008) A chaos-based visual encryption mechanism in integrated ECG/EEG medical signals. In: 2008 10th International Conference on Advanced Communication Technology, vol 3. IEEE, pp 1903–1907. https://doi.org/10.1109/ICACT.2008.4494157

  26. Lin CF, Chung CS (2007) A chaos base visual encryption mechanism in ECG medical signal. In: IFMBE Proceedings, vol 14. Springer, pp 2366–2369. https://doi.org/10.1007/978-3-540-36841-0_596

  27. Lin CF, Wang BS (2011) A 2D chaos-based visual encryption scheme for clinical EEG signals. J Mar Sci Technol 19(6):666–672. https://doi.org/10.51400/2709-6998.2209

    Article  Google Scholar 

  28. Lin CF, Shih SH, Zhu JD (2014) Chaos based encryption system for encrypting electroencephalogram signals. J Med Syst. https://doi.org/10.1007/s10916-014-0049-6

    Article  Google Scholar 

  29. Liu L, Miao S (2018) A new simple one-dimensional chaotic map and its application for image encryption. Multimed Tools Appl 77(16):21445–21462. https://doi.org/10.1007/s11042-017-5594-9

    Article  Google Scholar 

  30. Mboupda Pone JR, Çiçek S, Takougang Kingni S et al (2020) Passive-active integrators chaotic oscillator with anti-parallel diodes: analysis and its chaos-based encryption application to protect electrocardiogram signals. Analog Integr Circ Sig Process 103(1):1–15. https://doi.org/10.1007/s10470-019-01557-0

    Article  Google Scholar 

  31. Moumen A, Bouye M, Sissaoui H (2015) New secure partial encryption method for medical images using graph coloring problem. Nonlinear Dyn 82(3):1475–1482. https://doi.org/10.1007/s11071-015-2253-4

    Article  MathSciNet  Google Scholar 

  32. Murillo-Escobar MA, Cardoza-Avendaño L, López-Gutiérrez RM et al (2017) A double chaotic layer encryption algorithm for clinical signals in telemedicine. J Med Syst 41(4):1–17. https://doi.org/10.1007/s10916-017-0698-3

    Article  Google Scholar 

  33. Naik RB, Singh U (2022) A review on applications of chaotic maps in pseudo-random number generators and encryption. Ann Data Sci. https://doi.org/10.1007/s40745-021-00364-7

    Article  Google Scholar 

  34. Nusse HE, Yorke JA, Ditto WL (1995) Dynamics: numerical explorations. Phys.Today 48(7):57–57. https://doi.org/10.1063/1.2808104. http://link.springer.com/10.1007/978-1-4684-0231-5. https://pubs.aip.org/physicstoday/article-abstract/48/7/57/408637/Dynamics-Numerical-Explorations?redirectedFrom=fulltext

  35. Pandey A, Singh B, Saini BS et al (2019) A novel fused coupled chaotic map based confidential data embedding-then-encryption of electrocardiogram signal. Biocybern Biomed Eng 39(2):282–300. https://doi.org/10.1016/j.bbe.2018.11.012

    Article  Google Scholar 

  36. Ramakrishnan B, Nkandeu Kamdeu PY, Natiq H et al (2022) Image encryption with a Josephson junction model embedded in FPGA. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-12400-6

    Article  Google Scholar 

  37. Ravichandran D, Aashiq Banu S, Murthy BK et al (2021) An efficient medical image encryption using hybrid DNA computing and chaos in transform domain. Med Biol Eng Comput 59:589–605. https://doi.org/10.1007/s11517-021-02328-8

    Article  Google Scholar 

  38. Robinson V, Varghese EB (2016) A novel approach for ensuring the privacy of EEG signals using application-specific feature extraction and AES algorithm. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol 2. IEEE, pp 1–6. https://doi.org/10.1109/INVENTIVE.2016.7824799

  39. Sachin Singh P (2022) A novel chaotic Umbrella map and its application to image encryption. Opt Quant Electron 54(5):1–19. https://doi.org/10.1007/s11082-022-03646-3

    Article  Google Scholar 

  40. Sachin SP (2023) Asymmetric cryptosystem based on biological mutation operation in Chirp-Z domain. Multimed Tools Appl 82(27):42439–42463. https://doi.org/10.1007/s11042-023-15190-7

    Article  Google Scholar 

  41. Sachin S, Kumar R, Singh P (2021) Unequal modulus decomposition and modified Gerchberg Saxton algorithm based asymmetric cryptosystem in Chirp-Z transform domain. Opt Quant Electron 53(5):1–20. https://doi.org/10.1007/s11082-021-02908-w

    Article  Google Scholar 

  42. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097. https://doi.org/10.1007/s00521-014-1597-x

    Article  Google Scholar 

  43. Sarosh P, Parah SA, Mohiuddin Bhat G (2021) Fast image encryption framework for medical images. In: 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, pp 149–154. https://doi.org/10.1109/ICIEM51511.2021.9445362

  44. Setiadi DRIM, Rijati N (2023) An Image Encryption Scheme Combining 2D Cascaded Logistic Map and Permutation-Substitution Operations. Computation 11(9):178. https://doi.org/10.3390/computation11090178. https://www.mdpi.com/2079-3197/11/9/178/htm

  45. Sharif A, Mollaeefar M, Nazari M (2017) A novel method for digital image steganography based on a new three-dimensional chaotic map. Multimed Tools Appl 76(6):7849–7867. https://doi.org/10.1007/s11042-016-3398-y

    Article  Google Scholar 

  46. Sharma SR, Kaur M, Singh B (2022) A self-adaptive Bald Eagle Search optimization algorithm with dynamic opposition-based learning for global optimization problems. Expert Syst. https://doi.org/10.1111/EXSY.13170

    Article  Google Scholar 

  47. Sobolewski JS (2003) Cyclic Redundancy check, Wiley, GBR, pp 476–479. https://doi.org/10.5555/1074100.1074303

  48. Sokouti M, Zakerolhosseini A, Sokouti B (2016) Medical image encryption: an application for improved padding based GGH encryption algorithm. Open Med Inf J 10(1):11–22. https://doi.org/10.2174/1874431101610010011

    Article  Google Scholar 

  49. Usman K, Juzoji H, Nakajima I, et al (2007) Medical image encryption based on pixel arrangement and random permutation for transmission security. In: 9th International Conference on e-Health Networking, Application and Services. IEEE, pp 244–247. https://doi.org/10.1109/HEALTH.2007.381640

  50. Vaseghi B, Mobayen S, Hashemi SS et al (2021) Fast reaching finite time synchronization approach for chaotic systems with application in medical image encryption. IEEE Access 9:25911–25925. https://doi.org/10.1109/ACCESS.2021.3056037

    Article  Google Scholar 

  51. Waller M, Stotler C (2018) Telemedicine: a primer. Curr Allergy Asthma Rep 18(10):54. https://doi.org/10.1007/s11882-018-0808-4

    Article  Google Scholar 

  52. Wang X, Jin C (2012) Image encryption using game of life permutation and PWLCM chaotic system. Opt Commun 285(4):412–417. https://doi.org/10.1016/j.optcom.2011.10.010

    Article  Google Scholar 

  53. Wang X, Yin S, Shafiq M et al (2022) A new v-net convolutional neural network based on four-dimensional hyperchaotic system for medical image encryption. Security and Communication Networks 2022:1–14. https://doi.org/10.1155/2022/4260804

    Article  Google Scholar 

  54. Wu Y, Noonan J, Agaian S (2011) NPCR and UACI randomness tests for image encryption. Cyber J Multidiscip J Sci Technol J Sel Areas Telecommun 2(1):31–38. 10.1.1.390.2127. https://citeseerx.ist.psu.edu/document?repid=rep1%26type=pdf&doi=2b479abce221135af6065f9f8352e09cbfb5733a

    Google Scholar 

  55. Wu Y, Zhou Y, Saveriades G, et al (2013) Local Shannon entropy measure with statistical tests for image randomness. Inf Sci 222:323–342. https://doi.org/10.1016/j.ins.2012.07.049. https://linkinghub.elsevier.com/retrieve/pii/S002002551200521X

  56. Xu Y, Yang Z, Li X et al (2020) Dynamic opposite learning enhanced teaching-learning-based optimization. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2019.104966

    Article  Google Scholar 

  57. Yavuz E (2019) A novel chaotic image encryption algorithm based on content-sensitive dynamic function switching scheme. Opt Laser Technol 114(December 2018):224–239. https://doi.org/10.1016/j.optlastec.2019.01.043

    Article  Google Scholar 

  58. Yavuz E (2021) A new parallel processing architecture for accelerating image encryption based on chaos. J Inf Secur Appl 63(103):056. https://doi.org/10.1016/j.jisa.2021.103056

    Article  Google Scholar 

  59. Ye G, Jiao K, Pan C et al (2018) An effective framework for chaotic image encryption based on 3D logistic map. Secur Commun Netw. https://doi.org/10.1155/2018/8402578

    Article  Google Scholar 

  60. Zaman J, Ghosh R (2012) Review on fifteen Statistical Tests proposed by NIST. Ijtpc 1(November):18–31. www.IJTPC.org. http://www.doaj.org/doaj?func=fulltext &aId=1419927

  61. Zhang YQ, He Y, Li P et al (2020) A new color image encryption scheme based on 2DNLCML system and genetic operations. Opt Lasers Eng. https://doi.org/10.1016/j.optlaseng.2020.106040

    Article  Google Scholar 

  62. Zhou Y, Bao L, Chen CP (2014) A new 1d chaotic system for image encryption. Signal Process 97:172–182. https://doi.org/10.1016/j.sigpro.2013.10.034

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, 148106, India

    Suvita Rani Sharma & Birmohan Singh

  2. Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, 148106, India

    Manpreet Kaur

Authors
  1. Suvita Rani Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Birmohan Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Birmohan Singh.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, S.R., Singh, B. & Kaur, M. Improvement of medical data security using SABES optimization algorithm. J Supercomput 80, 12929–12965 (2024). https://doi.org/10.1007/s11227-024-05937-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-05937-w

Keywords

Search

Navigation