Purging of untrustworthy recommendations from a grid

TL;DR

This paper addresses the challenge of identifying and removing untrustworthy recommendations in grid computing to prevent misuse of resources, proposing a mechanism to enhance trustworthiness.

Contribution

It introduces a novel method for detecting and purging malicious indirect trust recommendations in grid environments, improving resource security.

Findings

Effective identification of untrustworthy recommendations

Reduction in malicious resource misuse

Enhanced trust accuracy in grid computing

Abstract

In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.