Abstract

Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignored and a fixed model is built for all the users. In fact, different users probably take different trust features into their considerations when making trust decisions. Besides, in the present schemes, only users’ static features are mapped into trust values, without the risk of privacy leakage. In this article, the features that each user cares about when making trust decisions are mined by machine learning to be User-Will. The privacy leakage risk of the evaluated user is estimated through information flow predicting. Then the User-Will and the privacy leakage risk are all mapped into trust evidence to be combined by an improved evidence combination rule of the evidence theory. In the end, several typical methods and the proposed scheme are implemented to compare the performance on dataset Epinions. Our scheme is verified to be more advanced than the others by comparing the F-Score and the Mean Error of the trust evaluation results.

Conclusion

In this article, a new trust evaluation scheme based on evidence theory is proposed. Our study achieved a better performance by focusing the issues as follows:

1. Determine the importance degree of each user features that each user cares about in making the decision to realize the individualization of trust evaluation.

2. Quantifying the risk of privacy leakage by information flow prediction to make the trust evaluation more comprehensive.

3. Use trust evidence to indicate the probability of trust, probability of distrust, and probability of ambiguity at the same time.

Compared with the existing methods, our proposed method achieves the highest accuracy and minimal error in the dataset Epinions. However, the weight part of User-Will does not contain the weight of trust evidence based on information flow risk, and the weight determination of this evidence will be our future work.