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.
Highlights
Online social networks (OSNs) are platforms or systems that people can interact with others by sharing or posting blogs online.[1]
Aiming at the above problems, we provide an improved scheme to evaluate the trust between the users in OSNs
Column MY_ID stands for Algorithm 2: Generating trust evidence based on information flow prediction
Summary
Online social networks (OSNs) are platforms or systems that people can interact with others by sharing or posting blogs online.[1] Social networking is very common, such as Facebook, Tweeter, Weibo and CyVOD.[2] These platforms provide a free space for everyone to unleash their mind and thoughts It makes information leakage possible.[3] The spammers spread malicious links and annoying messages to OSN users without target, and privacy information is unsafe for the cheating actions[4] and blackmails.[5] To prevent the malicious activities, many schemes such as Access Control[6] and digital rights protection[7,8,9] are proposed. Monte-Carlo simulation (MCS)[27] is used to predict the information flow probability between each two users to make the result infinitely close to the real value
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