Intelligent Security and Privacy of Electronic Health Records Using Biometric Images

Jaafar    M.    Alghazo

doi:10.2174/1573405615666181228121535

Abstract

Background: In the presence of Cloud Environment and the migration of Electronic Health Systems and records to the Cloud, patient privacy has become an emergent problem for healthcare institutions. Government bylaws, electronic health documentation, and innovative internet health services generate numerous security issues for healthcare conformity and information security groups. To deal with these issues, healthcare institutes must protect essential IT infrastructure from unauthorized use by insiders and hackers. The Cloud Computing archetype allows for EHealth methods that improve the features and functionality of systems on the cloud. On the other hand, sending patients’ medical information and records to the Cloud entails a number of risks in the protection and privacy of the health records during the communication process.

Aim: In this paper, a solution is proposed for the security of Electronic Health Records (EHRs) in cloud environment during the process of sending the data to the cloud. In addition, the proposed method uses biometric images that allow for unified patient identification across cloud-based EHRs and across medical institutions.

Method: To protect the privacy of patients’ information and streamline the migration process, a watermarking-based method is proposed for health care providers to ensure that patients’ data are only accessible to authorized personnel. Patients’ information, such as name, id, symptoms, diseases, and previous history, is secured in biometric images of patients as an encrypted watermark.

Results: Quality and impeccability analysis and robustness were performed to test the proposed method. The PSNR values show that the proposed method produced excellent results.

Conclusion: The robustness and impressibility of the proposed method were tested by subjecting the watermarked images to different simulated attacks. The watermarks were largely impermeable to varied and repeated attacks.

Keywords: Cloud computing, digital watermarking, electronic health record, biometric images, fingerprint, Fast Discrete Curvelet Transform (FDCT), Singular Value Decomposition (SVD).

Graphical Abstract

[1] 
Low C, Chen YH. Criteria for the evaluation of a cloud-based hospital information system outsourcing provider. J Med Syst  2012; 36(6): 3543-53.
[2] 
Sahi MA, Abbas H, Saleem K, et al. Privacy preservation in ehealthcare
environments: State of the art and future directions.
IEEE Access 2018; 6: 464-78. 
[3] 
Eagleson R, Altamirano-Diaz L, McInnis A, et al. Implementation of clinical research trials using web-based and mobile devices: Challenges and solutions. BMC Med Res Methodol  2017; 17(1): 43.
[4] 
Kennedy J, Eberhart RC. Particle swarm optimization. Proceedings
of ICNN'95-International Conference on Neural Networks; 1995;
Perth, WA, Australia. IEEE 1995 
[5] 
Furht B, Escalante A. Handbook of cloud computing. New York: Springer 2010.
[6] 
Chen YY, Lu JC, Jan JK. A secure EHR system based on hybrid clouds. J Med Syst  2012; 36(5): 3375-84.
[7] 
Polyák T. Robust watermarking of video streams. Acta Polytech  2006; 46(4): 49-51.
[8] 
Dittmann J, Stabenau M, Steinmetz R. Robust MPEG video watermarking
technologies. Proceedings of the sixth ACM international
conference on Multimedia; Bristol, United Kingdom. ACM
1998. 
[9] 
Zhu X, Girod B. Video streaming over wireless networks. In: 15th
European Signal Processing Conference; 2007; Poznan, Poland.
IEEE; pp. 1462-6. 
[10] 
Ishtiaq M, Jaffar MA, Khan MA, Jan Z, Mirza AM. Robust and imperceptible watermarking of video streams for low power devices.In: Signal Processing, Image Processing and Pattern Recognition.  Springer, Berlin, Heidelberg 2009; pp. 177-84.
[11] 
Piva A, Barni M, Bartolini F, Cappellini V. DCT-based watermark
recovering without resorting to the uncorrupted original image. In:
Proceedings of International Conference on Image Processing; Santa
Barbara, CA, USA; 1997; pp. 520-3. 
[12] 
Kotz D, Fu K, Gunter C, Rubin A. Security for mobile and cloud frontiers in healthcare. Commun ACM  2015; 58(8): 21-3.
[13] 
Elhoseny M, Salama AS, Abdelaziz A, Riad AM. Intelligent systems based on loud computing for healthcare services: A survey. Int J Comput Intel Stud  2017; 6(2-3): 157-88.
[14] 
Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform  2008; 77(5): 291-304.
[15] 
Latif G, Iskandar DA, Alghazo J, Jaffar A. Improving brain MR image classification for tumor segmentation using phase congruency. Curr Med Imaging Rev  2018; 14(6): 914-22.
[16] 
Al-Asad JF, Khan AH, Latif G, Hajji W. QR based despeckling
approach for medical ultrasound images. Curr Med Imaging Rev
2018. Available from: (http://www.eurekaselect.com/node/164615/article/qr-based-despeckling-approach-for-medical-ultrasound-images)
[17] 
Poulymenopoulou M, Malamateniou F, Vassilacopoulos G. Emergency healthcare process automation using mobile computing and cloud services. J Med Syst  2012; 36(5): 3233-41.
[18] 
Buyya R, Ranjan R. Special section: Federated resource management in grid and cloud computing systems. Fut Gen Comp Syst  2010; 26(8): 1189-91.
[19] 
Bateman A, Wood M. Cloud computing. Bioinformatics  2009; 25(12): 1475.
[20] 
Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. ACM  2010; 53(4): 50-8.
[21] 
Khan A, Mirza AM, Majid A. Intelligent perceptual shaping of a digital watermark: Exploiting characteristics of human visual system. Int J Knowl-based Intel Eng Syst  2006; 10(3): 213-23.
[22] 
Jayabalan M, O’Daniel T. Access control and privilege management in electronic health record: A systematic literature review. J Med Syst  2016; 40(12): 261.
[23] 
Jayabalan M, O’Daniel T. Continuous and transparent access control
framework for electronic health records: A preliminary study.
In: 2nd International conferences on Information Technology, Information
Systems and Electrical Engineering (ICITISEE); 2017
Yogyakarta, Indonesia 2017; IEEE; pp. 165-70. 
[24] 
Premarathne U, Abuadbba A, Alabdulatif A, et al. Hybrid cryptographic
access control for cloud-based EHR systems. IEEE Cloud
Comp 2016; (4): 58-64. 
[25] 
Daman R, Tripathi MM. Encryption tools for secured health data in public cloud. Int J Innovat Sci Eng Technol  2015; 2(11): 843-8.
[26] 
Qin Z, Weng J, Cui Y, Ren K. Privacy-preserving image processing in the cloud. IEEE Cloud Comp  2018; 5(2): 48-57.
[27] 
Coello CA, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput  2004; 8(3): 256-79.
[28] 
Han J, Zhao X, Qiu C. A digital image watermarking method based on host image analysis and genetic algorithm. J Amb Intel Human Comp  2016; 7(1): 37-45.
[29] 
Starck JL, Candès EJ, Donoho DL. The curvelet transform for image denoising. IEEE Trans Image Process  2002; 11(6): 670-84.
[30] 
Candes E, Demanet L, Donoho D, Ying L. Fast discrete curvelet transforms. Multiscale Model Simul  2006; 5(3): 861-99.
[31] 
AlZubi S, Islam N, Abbod M. Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation. J Biomed Imaging  2011; 2011: 4.
[32] 
Narayanan CS, Durai SA. A critical study on encryption based compression techniques. JCP  2016; 11(5): 380-99.
[33] 
Hammouche AM, El-Bakry HM, Mostafa RR. Image contrast enhancement using Fast Discrete Curvelet Transform via Unequally Spaced Fast Fourier Transform (FDCT-USFFT). IJECE  2016; 7(2): 2278-4209.
[34] 
Hammouche AM, El-Bakry HM, Mostafa RR. Image contrast enhancement using Fast Discrete Curvelet Transform via Unequally Spaced Fast Fourier Transform (FDCT-USFFT). Int J Electron Commun Comput Eng  2016; 8(3): 36-42.
[35] 
Mohammed AN, Taha TE, Farag Allah OS. Image fusion using FDCT based on SVD for CT/MRI medical images. MJEER  2014; 23: 37-51.
[36] 
Lumini A, Nanni L. When fingerprints are combined with Iris-A case study: FVC2004 and CASIA. IJCSNS  2007; 4(1): 27-34.
[37] 
Prior FW, Clark K, Commean P, et al. TCIA: An information resource to enable open science. Conf Proc IEEE Eng Med Biol Soc  2013; 2013: 1282-5.
[38] 
Singh AK, Kumar B, Singh G, Mohan A. In: Medical image watermarking.  Springer, Cham 2017; pp. 61-93.
[39] 
Sharma SG, Raheja LR. Efficient Algorithms for Embedding Digital
Watermark in Curvelet Domain. PhD Thesis, Thapar Institute of
Engineering and Technology: Patiala, October 2007. 
[40] 
Mousavi SM, Naghsh A, Abu-Bakar SA. Watermarking techniques used in medical images: A survey. J Digit Imaging  2014; 27(6): 714-29.
[41] 
Kaur KN, Gupta I, Singh AK. Digital image watermarking using (2, 2) visual cryptography with DWT-SVD based watermarking. In: Ehera H, Nayak J, Naik B, Abraham A, Eds. Computational Intelligence in Data Mining Advances in Intelligent Systems and Computing.  Springer, Singapore 2018; pp. 77-86.

Rights & Permissions Print Cite

Article Metrics

38

4

1

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

For Visitors

DOI https://dx.doi.org/10.2174/1573405615666181228121535	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603

Current Medical Imaging

Intelligent Security and Privacy of Electronic Health Records Using Biometric Images

Abstract Play Pause

Graphical Abstract

Related Books

Abstract