JOURNAL ARTICLE

A Novel High-Speed Data Encryption Scheme for Internet of Medical Things Using Modified Elliptic Curve Diffie–Hellman and Advance Encryption Standard.

  • Published In: International Journal of Image & Graphics, 2024, v. 24, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Prathibha, L.; Fatima, Kaleem 3 of 3

Abstract

The security of data in an IoT network is of utmost importance. The medical IoT devices need to implement the security protocols in the devices to safeguard the crucial information. This paper proposes a novel high-speed data encryption scheme using elliptic curve cryptography (ECC) for key generation, key length reduction, Diffie–Hellman key exchange, SHA-256 and advance encryption standard (AES). The proposed hybrid data encryption scheme begins with elliptic curve key generation. Each authorized user in the broadcast range generated a private key using ECC. The proposed key reduction algorithm reduces the size of the secret key generated. This key is exchanged efficiently between the broadcast members using Diffie–Hellman key exchange. Each user then generates their final encryption keys. These keys are encoded using SHA-256 algorithm for further security. These keys are then used to encrypt the data using AES algorithm and sent to the cloud. The intended receivers can check the identity of the sender and decrypt the data using their own keys. The proposed method also includes user identity authentication. The origin of the message is verified to authenticate the sender of the message. This implementation is apt for home automation applications where data collected by medical devices like Fitbit and watches need to be shared among multiple users. The proposed method is at par in providing security to the data in much less time. The throughput, key generation time, key length, encryption time, decryption time and memory usage, Avalanche effect are efficient in comparison to existing methods. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Image & Graphics. 2024/09, Vol. 24, Issue 5, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2024
  • ISSN:0219-4678
  • DOI:10.1142/S0219467823400041
  • Accession Number:180249313
  • Copyright Statement:Copyright of International Journal of Image & Graphics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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