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A Differential-Privacy-Based Blockchain Architecture to Secure and Store Electronic Health Records

Published: 20 July 2021 Publication History

Abstract

As humanity enters the information age, the amount of digitized personal information grows daily. With growing connections between the digital and real-world, privacy information becomes more and more at risk, most especially information pertaining to one's electronic health records, or EHRs. The consequences of improper EHR security are shown in [12] and [13]. Innovations in security, namely Blockchain and differential privacy, provide data centers a powerful tool to combat would-be belligerents and secure patient data. We propose a novel blockchain architecture that utilizes the discrete M-band wavelet transform with Laplace-Sigmoid noise that allows connected centers to perform relevant research while also securing sensitive EHRs and protecting patient identities. We then simulate training machine learning models using our system and show that they perform with high accuracy.

References

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Y. Sun, L. Zhang, G. Feng, B. Yang, B. Cao and M. A. Imran, "Blockchain-Enabled Wireless Internet of Things: Performance Analysis and Optimal Communication Node Deployment," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5791-5802, June 2019.
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Choi, K., & Lee, T. (2019). Differentially Private M-Band Wavelet-Based Mechanisms in Machine Learning Environments. S.-T. Yau High School Science Award (Computer).
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Dankar, Fida & Emam, Khaled. (2013). Practicing Differential Privacy in Health Care: A Review. Transactions on Data Privacy. 6. 35-67.
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Cynthia Dwork; Aaron Roth, "The Algorithmic Foundations of Differential Privacy," in The Algorithmic Foundations of Differential Privacy, now, 2014.
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Dwork, C. (2008). Differential Privacy: A Survey of Results. Lecture Notes in Computer Science Theory and Applications of Models of Computation, 1-19.
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W. Liu, S. S. Zhu, T. Mundie and U. Krieger, "Advanced block-chain architecture for e-health systems," 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, 2017, pp. 1-6.
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Ul Hassan, Muneeb & Rehmani, Mubashir Husain & Chen, Jinjun. (2019). Differential Privacy in Blockchain Technology: A Futuristic Approach.
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Li, S., & Ma R. (2017). Quantum Machine Learning Algorithm Based on M-band Wavelet Kernel SVM and its Applications on Pattern Recognition. S.-T. Yau High School Science Award (Mathematics)
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Ma, R., Liu, T., Li S., & Wang, X. (2018). "M-band Wavelet Kernels for Classical and Quantum SVM," 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), Chongqing, China, 2018, pp. 515-521.
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Lee J., Clifton C. (2011) How Much Is Enough? Choosing ɛ for Differential Privacy. In: Lai X., Zhou J., Li H. (eds) Information Security. ISC 2011. Lecture Notes in Computer Science, vol 7001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24861-0_22
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J. Hsu, "Differential Privacy: An Economic Method for Choosing Epsilon," 2014 IEEE 27th Computer Security Foundations Symposium, Vienna, 2014, pp. 398-410.
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Nguyen, A. (2019, July 06). Understanding Differential Privacy. https://towardsdatascience.com/understanding-differential-privacy-85ce191e198a

Cited By

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  • (2024)A Manifesto for Healthcare Based Blockchain: Research Directions for the Future GenerationJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01074-3105:5(1429-1450)Online publication date: 24-May-2024
  • (2023)Have the cake and eat it too: Differential Privacy enables privacy and precise analyticsJournal of Big Data10.1186/s40537-023-00712-910:1Online publication date: 14-Jul-2023
  • (2022)Leveraging Block Chain for Ensuring Trust in IoT in Health CareInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-3171(175-180)Online publication date: 16-Apr-2022
  • Show More Cited By

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cover image ACM Other conferences
ICBCT '21: Proceedings of the 2021 3rd International Conference on Blockchain Technology
March 2021
216 pages
ISBN:9781450389624
DOI:10.1145/3460537
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2021

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Author Tags

  1. Blockchain
  2. Differential Privacy
  3. Electronic Health Records
  4. Machine Learning

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Cited By

View all
  • (2024)A Manifesto for Healthcare Based Blockchain: Research Directions for the Future GenerationJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01074-3105:5(1429-1450)Online publication date: 24-May-2024
  • (2023)Have the cake and eat it too: Differential Privacy enables privacy and precise analyticsJournal of Big Data10.1186/s40537-023-00712-910:1Online publication date: 14-Jul-2023
  • (2022)Leveraging Block Chain for Ensuring Trust in IoT in Health CareInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-3171(175-180)Online publication date: 16-Apr-2022
  • (2022) ShareChain : Blockchain‐enabled model for sharing patient data using federated learning and differential privacy Expert Systems10.1111/exsy.1313140:5Online publication date: 24-Aug-2022
  • (2022)Exploiting smart contracts in PBFT-based blockchainsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109003211:COnline publication date: 5-Jul-2022

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