从网格中清除不可信的建议
Purging of untrustworthy recommendations from a grid
摘要：
在网格计算中，信任具有重要的意义。有大量的研究提出了各种模型，在提供可信的资源共享机制。信任是一种信念或感知，许多研究人员试图将其与某种计算模型联系起来。对任何实体的信任可以是直接的或间接的。直接信任是对实体的第一印象或在一些直接互动中获得的影响。间接信任是信任可能是由于从网格中的特定域或该网格之外或该网格本身之外的任何其他域的各种商家获得的声誉或接收的推荐。不幸的是，恶意的间接信任导致了对网格宝贵资源的滥用。提出了网格环境下不可信推荐的识别和清除机制。通过得到的结果，我们展示了如何清除不可信的实体。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
