With the approach of telemedicine innovation, numerous restorative administrations can be given remotely, which significantly improves the welfare of our humankind. In any case, security and protection of medicinal information transmitted through media transmission frameworks remain a major issue to be settled while conveying such administrations. Specifically, the therapeutic pictures and information put away in the cloud or transmitted over uncertain channel, may endure from unapproved changes by vindictive aggressors. Henceforth, trustworthiness of such medicinal information is of most extreme significance for telemedicine applications. Cryptographic hash capacities (e.g. SHA-3) can be utilized to guarantee the respectability of therapeutic information imparted over uncertain channel.
Be that as it may, when the volume furthermore, size of medicinal information develops (e.g. high goals medicinal picture), it is troublesome for customary CPU-based framework to hash these information in convenient way. In perspective of that, we are roused to explore on enhanced usage methods of Keccak hash work in enormously parallel stages, as the aftereffect of such work can be utilized in enhancing the speed execution of telemedicine applications.
Graphical handling unit (GPU) is one of the rising stages with greatly parallel preparing control that can be saddled to take care of computational issues considerably quicker than ordinary CPUs. In this project, we present the effective usage of tree-mode Keccak-f(1600) in GPU what’s more, research the impact of parallel granularities by hashing one duplicate of Keccak stage work utilizing 1 string, 5 strings and 25 strings separately. We additionally proposed another procedure to execute tree-mode Keccak-f(1600) in light of Dynamic Parallelism offered in new NVIDIA GPU. Our test results demonstrate that parallel granularity of one string produces the most astounding hash throughput at 28.51 Gbps. The high hash rate of such usage can significantly improve the respectability check for restorative information in telemedicine applications.