What are the various trust models

A Generalized Trust Model
At the bottom, we identify three major factors which influence the trustworthiness of a resource site. An inference module is required to aggregate these factors. Followings are some existing inference or aggregation methods. An intra-site fuzzy inference procedure is called to assess defense capability and direct reputation. Defense capability is decided by the firewall, intrusion detection system (IDS), intrusion response capability, and anti-virus capacity of the individual resource site. Direct reputation is decided based on the job success rate, site utilization, job turnaround time, and job slowdown ratio measured. Recommended trust is also known as secondary trust and is obtained indirectly over the grid network.
Reputation-Based Trust Model
In a reputation-based model, jobs are sent to a resource site only when the site is trustworthy to meet users’ demands. The site trustworthiness is usually calculated from the following information: the defense capability, direct reputation, and recommendation trust.
The defense capability refers to the site’s ability to protect itself from danger. It is assessed according to such factors as intrusion detection, firewall, response capabilities, anti-virus capacity, and so on. Direct reputation is based on experiences of prior jobs previously submitted to the site. The reputation is measured by many factors such as prior job execution success rate, cumulative site utilization, job turnaround time, job slowdown ratio, and so on. A positive experience associated with a site will improve its reputation. On the contrary, a negative experience with a site will decrease its reputation.
A Fuzzy-Trust Model
In this model , the job security demand (SD) is supplied by the user programs. The trust index (TI) of a resource site is aggregated through the fuzzy-logic inference process over all related parameters. Specifically, one can use a two-level fuzzy logic to estimate the aggregation of numerous trust parameters and security attributes into scalar quantities that are easy to use in the job scheduling and resource mapping process.
The TI is normalized as a single real number with 0 representing the condition with the highest risk at a site and 1 representing the condition which is totally risk-free or fully trusted. The fuzzy inference is accomplished through four steps: fuzzification, inference, aggregation, and defuzzification.
The second salient feature of the trust model is that if a site’s trust index cannot match the job security demand (i.e., SD > TI), the trust model could deduce detailed security features to guide the site security upgrade as a result of tuning the fuzzy system.