Comparative Assessment on Privacy Preservation in Health Care Sectors coupled with IoT
Pravin N.Kathavate1, J. Amudhavel2
1Mr. Pravin N.Kathavate, Research Scholar, K L University Deemed To be University, Vaddeswaram, Guntur (Andhra Pradesh), India.
2Dr. J. Amudhavel, Research Supervisor, K L University Deemed To be University), Vaddeswaram, Guntur (Andhra Pradesh), India.
Manuscript received on 09 February 2019 | Revised Manuscript received on 22 February 2019 | Manuscript Published on 04 March 2019 | PP: 467-475 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2085017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Safe and high-quality healthcare service is of supreme significance to patients. Security and patients’ privacy of healthcare data are imperative problems that will have a large impact on the upcoming accomplishment of Healthcare with IoT. A major problem in the IoT dependent healthcare system is the fortification of privacy. Usually, a healthcare service contributor receives data from its patients and distributes them with healthcare experts or registered clinics. The contributor may perhaps share out the data to pharmaceutical companies and health insurance companies. Hence, for overcoming the challenges existing in security, this paper has come out with a privacy-preserving technique with significant data extraction from IoT devices linked with healthcare sector. According to the adopted scheme, the information obtained from IoT devices is processed for preserving the sensitive data, such that unknown people are prohibited to access them. Here, Grey Wolf Optimization (GWO) scheme is proposed to recognize the optimal key. The objective of the proposed scheme is to minimize hiding failure rate, modification degree, and true positive value for better preservation of sensitive data. Moreover, the implemented technique is distinguished with conventional schemes like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC), Firefly (FF) and Differential Evolution (DE) algorithms in terms of performance. Also, the statistical analysis of the presented method is measured for three test cases, and the effectiveness of the implemented method is revealed.
Keywords: Internet of Things; Healthcare; Privacy Preservation; Sanitization; Hidden Rate; Modification Degree, True Positive Rate.
Scope of the Article: IoT