Hybrid bioinspired approach for secret sharing algorithm and ownership transfer optimization in cloud based models

Abstract

It is widely accepted that secret sharing models like Shamir cryptosystem allows cloud designers to deploy highly secure multichannel data transfer mechanisms. But these systems are majorly static, and do not incorporate context-aware share-selection techniques. Moreover, SaaS based cloud deployments require ownership transfers for efficient real-time operations. These ownership transfer mechanisms are backed by selection encryption models assisting in permission-aware data sharing. Scholars have put forward pre-emptive and bioinspired models to accomplish these tasks, but nearly all of them are still sophisticated or demonstrate limited flexibility in real-time scenarios. The proposed structure first gathers cloud logs for various temporal requests and groups them based on IP & resource constraints with the goal of getting around these problems. The modified rules are initially tested on dummy ownership requests, and are later on real-time ownership-transfer scenarios for validation of their efficiency levels based on accuracy of misconfiguration detection, delay needed for processing the requests, consistency of blocking invalid ownership requests, and request throughput levels. Based on this evaluation, The accuracy might be increased by 3.5% using the suggested model, reducing delay by 2.9%, while having 6.4% higher consistency, and 8.3% higher throughput than existing secret sharing & ownership transfer methods under similar deployment scenarios.