Abstract
Privacy protection in mobile social networks is a hot issue in current research. Various privacy protection policies have been proposed. However, the conflict of privacy protection policies inevitably occurs. In this article, aiming at the personalized privacy protection model proposed in our published paper, we analyzed the possible conflict between privacy policies and comprehensively considered the policy conflict brought by the resource-level relationship; meanwhile, we proposed a scheme of consistency verification for privacy policy to improve the previous personalized privacy protection model. We also verified the practical effects of the improved model by experiments on synthetic data sets.
Highlights
With the wide application of mobile devices and Web 2.0 techniques, mobile social networks (MSNs) have experienced exponential growth in recent years
We summarized the main access control models in MSN, analyzed their contribution, and point out their disadvantages
Due to the overlapping or hierarchical relationship among rules’ subject attributes, resource attributes, and action attributes, there may be logical inconsistencies in the formulation of privacy policies, for example, both positive authorization and negative authorization may exist on the same subject and object in different strategies, which will result in the privacy policy conflict
Summary
With the wide application of mobile devices and Web 2.0 techniques, mobile social networks (MSNs) have experienced exponential growth in recent years. Due to the overlapping or hierarchical relationship among rules’ subject attributes, resource attributes, and action attributes, there may be logical inconsistencies in the formulation of privacy policies, for example, both positive authorization and negative authorization may exist on the same subject and object in different strategies, which will result in the privacy policy conflict. The specific process is shown, which can be divided into the following steps: (1) users define personalized privacy policy; (2) design access authorization reasoning rules and policy conflict rules according to privacy policy; (3) realize user queries on policy permission assignment and policy conflict; (4) according to conflict query request, call logical transformer to convert data and privacy policies stored in relational databases into facts; (5) reasoning engine completes automatic reasoning of user authorization and policy conflict based on existing facts and reasoning rules; and (6) present the result of policy conflict, and the conflict strategy is corrected by interacting with users. Different table logic transformation methods are called to convert the extracted data into fact statements
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