Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence.

H.S. Jennath; V.S. Anoop; S. Asharaf

doi:10.9781/ijimai.2020.07.002

Authors

H.S. Jennath Indian Institute of Information Technology and Management, Kerala
V.S. Anoop Indian Institute of Information Technology and Management, Kerala
S. Asharaf Indian Institute of Information Technology and Management, Kerala

DOI:

https://doi.org/10.9781/ijimai.2020.07.002

Keywords:

Blockchain, Health, Data Security, Data Privacy, Artificial Intelligence, Interoperability, Traceability

Supporting Agencies

This work is supported by the Back to Lab Programme research fellowship (Ref: Order No.1281/2016/KSCSTE) from Women Scientists Division (WSD), Kerala State Council for Science, Technology and Environment (KSCSTE).The authors would like to thank all the researchers, engineers and other staff members of Kerala Blockchain Academy and Data Engineering Lab at Indian Institute of Information Technology and Management-Kerala, for their valuable comments that significantly improved the quality of this paper. The authors also acknowledge the staff members for providing infrastructure facilities for conducting an experiment of this scale.

Abstract

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models.

Downloads

Download data is not yet available.

References

[1] T. Mcghin, K.-K. R. Choo, C. Z. Liu, and D. He, “Blockchain in healthcare applications: Research challenges and opportunities,” Journal of Network and Computer Applications, vol. 135, pp. 62–75, 2019.

[2] R. Reisman, “Blockchain Serverless Public/Private Key Infrastructure for ADS-B Security, Authentication, and Privacy,” AIAA Scitech 2019 Forum, 2019.

[3] M. Chanson, A.Bilgeri, D., Fleisch, E., and Wortmann, F. “PrivacyPreserving Data Certification in the Internet of Things: Leveraging Blockchain Technology to Protect Sensor Data,” Journal of the Association for Information Systems, 2019.

[4] A. A. Omar, M. Z. A. Bhuiyan, A. Basu, S. Kiyomoto, and M. S. Rahman, “Privacy-friendly platform for healthcare data in cloud based on blockchain environment,” Future Generation Computer Systems, vol. 95, pp. 511–521, 2019.

[5] A. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “A Decentralized Privacy-Preserving Healthcare Blockchain for IoT,” Sensors, vol. 19, no. 2, p. 326, 2019.

[6] M. S. Gross and R. C. Miller, “Ethical Implementation of the Learning Healthcare System with Blockchain Technology,” Blockchain in Healthcare Today, vol. 2, 2019.

[7] H. Tian, J. He, and Y. Ding, “Medical Data Management on Blockchain with Privacy,” Journal of medical systems, vol. 43, no. 2, article 26, 2019.

[8] Y. Chen, S. Ding, Z. Xu, H. Zheng, and S. Yang, “Blockchain-Based Medical Records Secure Storage and Medical Service Framework,” Journal of Medical Systems, vol. 43, no. 1, 2018.

[9] W. J. Gordon and C. Catalini, “Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability,” Computational and Structural Biotechnology Journal, vol. 16, pp. 224–230, 2018.

[10] T. T. Kuo, and L. Ohno-Machado (2018), “Modelchain: Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks,” arXiv preprint arXiv:1802.01746.

[11] M. A. Cyran, “Blockchain as a Foundation for Sharing Healthcare Data,” Blockchain in Healthcare Today, 2018.

[12] C. Catalini and J. Gans, “Some Simple Economics of the Blockchain,” 2016.

[13] S. Nakamoto“Bitcoin: A peer-to-peer electronic cash system.”, 2008

[14] G. A. Montes and B. Goertzel, “Distributed, decentralized, and democratized artificial intelligence,” Technological Forecasting and Social Change, vol. 141, pp. 354–358, 2019.

[15] What Is Patient Engagement? | Evariant: The Leading Healthcare CRM Solution. [online] Available at: [Accessed 23 June 2020].

[16] H. Löhr, A.-R. Sadeghi, and M. Winandy, “Securing the e-health cloud,” Proceedings of the ACM international conference on Health informatics - IHI 10, 2010.

[17] D. Kotz, C. A. Gunter, S. Kumar, and J. P. Weiner, “Privacy and Security in Mobile Health: A Research Agenda,” Computer, vol. 49, no. 6, pp. 22–30, 2016.

[18] M.A.Sahi, H.Abbas, K.Saleem, X.Yang, A.Derhab, M.A.Orgun, W. Iqbal, I. Rashid, and A. Yaseen, “ Privacy preservation in e-healthcare environments: State of the art and future directions,” IEEE Access, vol. 6, pp.464-478. 2017.

[19] M. Panner, “Blockchain In Healthcare: How It Could Make Digital Healthcare Safer And More Innovative,” 2019, [Accessed 23 June 2020].

[20] W. J. Gordon and C. Catalini, “Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability,” Computational and Structural Biotechnology Journal, vol. 16, pp. 224–230, 2018.

[21] W.J. Gordon, A. Wright and A. Landman. Blockchain Technology in Health Care: Decoding the hype. NEJM Catal2017 https://catalyst.nejm.org/decoding-blockchaintechnology-health/, Accessed date: 28 March 2018.

[22] A. J. Holmgren, V. Patel, and J. Adler-Milstein, “Progress In Interoperability: Measuring US Hospitals’ Engagement In Sharing Patient Data,” Health Affairs, vol. 36, no. 10, pp. 1820–1827, 2017.

[23] Q. Nasir, I.A. Qasse, M. Abu Talib, and A.B Nassif, “Performance analysis of hyperledger fabric platforms,” Security and Communication Networks, 2018.

[24] A. Brando,H. So Mamede,and R. Gonalves (2019, April), “Trusted Dataset Marketplace,” In World Conference on Information Systems and Technologies (pp. 515-527). Springer, Cham.

[25] C. Cachin (2016, July), “Architecture of the hyperledger blockchain fabric,” In Workshop on distributed cryptocurrencies and consensus ledgers (Vol. 310, No. 4).

[26] E. Androulaki, A. Barger, V. Bortnikov, C. Cachin, K. Christidis, A. D. Caro, D. Enyeart, C. Ferris, G. Laventman, Y. Manevich, S. Muralidharan, C. Murthy, B. Nguyen, M. Sethi, G. Singh, K. Smith, A. Sorniotti, C. Stathakopoulou, M. Vukolić, S. W. Cocco, and J. Yellick, “Hyperledger fabric,” Proceedings of the Thirteenth EuroSys Conference, 2018.

[27] V. Dhillon, D. Metcalf, and M. Hooper, “The Hyperledger Project,” Blockchain Enabled Applications, pp. 139–149, 2017.M. Swan (2015). Blockchain: Blueprint for a new economy. “O’Reilly Media, Inc.”

[28] Gorenflo, Christian, Stephen Lee, Lukasz Golab, and Srinivasan Keshav. “Fastfabric: Scaling hyperledger fabric to 20,000 transactions per second.” In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 455-463. IEEE, 2019.

[29] Benhamouda, F., Halevi, S. and Halevi, T., 2019. Supporting private data on hyperledger fabric with secure multiparty computation. IBM Journal of Research and Development, 63(2/3), pp. 3:1-3:8.

[30] K.Olson, M.Bowman, J. Mitchell, S. Amundson, D. Middleton, and C. Montgomery, “Sawtooth: An Introduction. The Linux Foundation,” 2018.

[31] I. C. Lin and T. C. Liao, “A survey of blockchain security issues and challenges,” IJ Network Security, vol. 19, no. 5, 653-659. 2017.

[32] Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. “MIMIC-III, a freely accessible critical care database,” Scientific Data, vol. 3, article 160035, 2016.

[33] Johnson, A., Pollard, T., & Mark, R. (2019). MIMIC-III Clinical Database Demo (version 1.4). PhysioNet. https://doi.org/10.13026/C2HM2Q.

[34] H. Oh, A. Jadad, C. Rizo, M. Enkin, J. Powell, and C. Pagliari, “What Is eHealth (3): A Systematic Review of Published Definitions,” Journal of Medical Internet Research, vol. 7, no. 1, 2005.

[35] X. Yue, H. Wang, D. Jin, M. Li, and W. Jiang, “Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control,” Journal of Medical Systems, vol. 40, no. 10, 2016.

[36] D. Ivan. “Moving toward a blockchain-based method for the secure storage of patient records”, NIST/ONC; 2016.

[37] WJ. Gordon, A. Landman, “Secure, decentralized, interoperable medication reconciliation using the Blockchain”,NIST/ONC, 2016.

[38] G. Zyskind, O. Nathan, and A. sandy Pentland, “Decentralizing Privacy: Using Blockchain to Protect Personal Data,” 2015 IEEE Security and Privacy Workshops, 2015.

[39] “Health Level Seven International,” Health Level Seven International - Homepage. [Online]. Available: http://www.hl7.org/. [Accessed: 22-Jun2020].

[40] P. Zhang, J. White, D. C. Schmidt, G. Lenz, and S. T. Rosenbloom, “FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data,” Computational and Structural Biotechnology Journal, vol. 16, pp. 267–278, 2018.

[41] K. Sarpatwar, R. Vaculin, H. Min, G. Su, T. Heath, G. Ganapavarapu, and D. Dillenberger, “Towards Enabling Trusted Artificial Intelligence via Blockchain,” Policy-Based Autonomic Data Governance Lecture Notes in Computer Science, pp. 137–153, 2019.

[42] A. Azaria, A. Ekblaw, T. Vieira, and A. Lippman, 2016, August. “Medrec: Using blockchain for medical data access and permission management,” In 2016 2nd International Conference on Open and Big Data (OBD) (pp. 25-30). IEEE.