Automatic trust calculation for service-oriented systems

Among various service providers providing identical or similar services with varying quality of service, trust is essential for service consumers to find the right one. Manually assigning feedback costs much time and suffers from several drawbacks. Only automatic trust calculation is feasible for large-scale service-oriented applications. Therefore an automatic method of trust calculation is proposed. To make the calculation accurate, the Kalman filter is adopted to filter out malicious non-trust quality criterion (NTQC) values instead of malicious trust values. To offer higher detection accuracy, it is further improved by considering the relationship between NTQC values and variances. Since dishonest or inaccurate values can still influence trust values, the similarity between consumers is used to weight data from other consumers. As existing models only used the Euclidean function and ignored others, a collection of distance functions is modified to calculate the similarity. Finally, experiments are carried out to access the robustness of the proposed model. The results show that the improved algorithm can offer higher detection accuracy, and it was discovered that another equation outperformed the Euclidean function.