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Efficient medical image security and transmission using modified LZW compression and ECDH-AES for telemedicine applications.
- Source :
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Soft Computing - A Fusion of Foundations, Methodologies & Applications . Jul2023, Vol. 27 Issue 13, p9151-9168. 18p. - Publication Year :
- 2023
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Abstract
- The healthcare industry is a part of contemporary civilization that deals with a significant volume of confidential information. The integration of an increased number of digital systems in hospitals, laboratories, and outpatient examinations of patients, as well as the files that result from these examinations, has largely been responsible for the massive increase in the amount of information that is associated with patient care over the past few years. Electronic health record systems are a kind of medical management system that record a patient's personal information in addition to their medical history. Consequently, the storage and protection of these data while it is held on the cloud is of the highest significance. In this work, an efficient data storage and protection system that is tailored to the internet of medical things is presented. The data, whether it is text or pictures, are compressed using a modified version of the lossless LZW compression algorithm, and then, they are stored locally before being sent to the cloud. After that, the data that were compressed are encrypted using the generated elliptic curve Diffie–Hellman key and the advance encryption standard. The suggested security strategy is a hybrid cryptography approach that incorporates both symmetric key cryptography and asymmetric key cryptography in order to provide an increased level of protection for the system. Because it is not a complex system and uses less resources than other alternatives, the suggested solution is suitable for porting onto edge devices. When compared to the traditional technique, the time needed to generate a key was cut by a factor of 1e4, and the amount of time needed to encrypt data was cut by 20%. The throughput of the model that was suggested improved by 87%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 27
- Issue :
- 13
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 163965204
- Full Text :
- https://doi.org/10.1007/s00500-023-08499-w