Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain

Authors

  • Andi STMIK TIME
  • Carles Juliandy STMIK TIME
  • Robet STMIK TIME
  • Octara Pribadi STMIK TIME

DOI:

https://doi.org/10.21512/commit.v16i1.7958

Keywords:

Medical Records, COVID-19, Elliptic Curve Digital Signature Algorithm (ECDSA), Blockchain Technology

Abstract

The rapid and dangerous spread of COVID-19 has forced governments in various countries to provide information on patients’ medical records to the public in the context of prevention. Meanwhile, patients’ medical records are vital and confidential because they contain patients’ privacy. Changing and falsifying a patient’s medical record leads to various dangerous consequences, such as mishandling which results in the patient’s death. From these problems, the research introduces a new model with a combination of blockchain technology and the Elliptic Curve Digital Signature Algorithm (ECDSA) to secure the medical records of COVID-19 patients. This model is an improvement from the model and framework proposed by previous researchers. The proposed model consists of two big parts (front and back end). Then, the simulations are carried out to measure and prove the level of security of blockchain technology in securing patient medical records. The research results show that the ECDSA algorithm can protect patients’ medical records from being opened by unauthorized parties. Then, blockchain technology can prevent changes or manipulation of patient medical records because the information recorded on the blockchain network is impossible to change and will be immutable. The research has successfully introduced a new model in securing patient medical records.

Dimensions

Plum Analytics

Author Biographies

Andi, STMIK TIME

Informatics Engineering Department

Carles Juliandy, STMIK TIME

Informatics Engineering Department

Robet, STMIK TIME

Informatics Engineering Department

Octara Pribadi, STMIK TIME

Informatics Engineering Department

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Published

2022-03-31
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PDF downloaded 535  .