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Securing Internet-of-Things Data in a Healthcare Surveillance Network with a Hybrid Encryption Algorithm
Abstract
IoT-based healthcare solutions are used for remote health
monitoring and diagnostics to minimize the cost of health care
services. Secure data communication is crucial for treatment
and monitoring. Furthermore, it is vital to safeguard the
patient's information from abuse and data manipulation while
it is in transit because other devices can easily trace it. IoT
networks have limited CPU capability, memory, and power,
making performing the expensive operations required by
ciphering algorithms challenging. As a result, they require a
lightweight security approach that consumes fewer resources.
A hybrid cipher approach for safe data transport from IoT
healthcare devices is proposed in this paper. Three cipher
algorithms are used in the encryption process, including a
modified, lightweight Salsa20 with 16 words (each word is 32
bits). To begin, for each block of sensing data, a five-
dimensional chaotic map is employed to generate 32 keys of
64-bit length. Second, to make the produced keys appear
dynamic, random shifting is performed on them. Third, an
exclusive-or operation is carried out between a newly
constructed key, which includes sensing data, and modified
Salsa20 words. Fourth, the proposed method improves
security by using the addition DNA operation between the
result of the previous step and the random selection word in
Salsa20 state after both have been turned into DNA form.
Finally, before sending data to the server, sensor data is
hashed using the SHA3-256 hash method to assure data
integrity. The hash data result, the DNA result after
conversion to a decimal number, and two random integers for
shifting keys and picking Salsa20 words comprise the entire
delivered data. Based on tests done by NIST, the suggested
algorithms provide data content security features like secrecy,
authentication, and non-repudiation. They also work with all
types of sensors to send secure data to the final controller.
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DOI: https://doi.org/10.37628/ijtet.v9i1.1860
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