An Advanced Certain Trust Model Using Fuzzy Logic and Probabilistic Logic theory
This work addresses trustworthiness for users and developers in systems like cloud computing and e-commerce, but it appears incremental as it builds upon an existing Certain Trust Model.
The authors tackled the problem of accurately assessing trustworthiness in service-oriented systems by extending the Certain Trust Model with fuzzy and probabilistic logic, introducing new parameters (trust T and behavioral probability P) and demonstrating the model's verification through laboratory implementation and Fuzzy Associative Memory.
Trustworthiness especially for service oriented system is very important topic now a day in IT field of the whole world. Certain Trust Model depends on some certain values given by experts and developers. Here, main parameters for calculating trust are certainty and average rating. In this paper we have proposed an Extension of Certain Trust Model, mainly the representation portion based on probabilistic logic and fuzzy logic. This extended model can be applied in a system like cloud computing, internet, website, e-commerce, etc. to ensure trustworthiness of these platforms. The model uses the concept of fuzzy logic to add fuzziness with certainty and average rating to calculate the trustworthiness of a system more accurately. We have proposed two new parameters - trust T and behavioral probability P, which will help both the users and the developers of the system to understand its present condition easily. The linguistic variables are defined for both T and P and then these variables are implemented in our laboratory to verify the proposed trust model. We represent the trustworthiness of test system for two cases of evidence value using Fuzzy Associative Memory (FAM). We use inference rules and defuzzification method for verifying the model.