Mobile crowd-sensing context aware based fine-grained access control mode

Dengpan Ye, Yuan Mei, Yueyun Shang, Jixiang Zhu, Kun Ouyang

Abstract: The present of smart mobile devices have provided unprecedented flexibility to humankind, with which people are able to access kinds of system resource through internet everywhere, including confidential data, nevertheless. While the traditional computing environment is always considered to be static and security-guarded, the context of mobile computing is much more variable, complex, and risk-hidden. To provide appropriate protection on mobile devices, we proposed a context-aware model combined with crowd-sensing paradigm to achieve fine-grained measurement of user’s current context. Corresponding to the context-aware model, we categorize the context by kinds of attributes and proposed Attribute-tree based Context-Aware Access Control model to protect user’s privacy and confidential information. The experimental result indicates that our proposed model is fine-grained, efficient and flexible to apply to different mobile platforms.