Android uses the permission-based access control method to protect the system resources, which has the problem of rough management. At the same time, some malicious applications can secretly access resources in a privacy scenario without the user's permission, bringing certain threats to user privacy and system resources. Based on the original permission management and control and with the introduction of Access Control List (ACL) mechanism, an Android fine-grained permission management and control system based on ACL mechanism was designed and implemented. The proposed system can dynamically set the access rights of the applications according to the user's policy, avoiding the access of malicious codes to protect system resources. Tests of compatibility and effectiveness show that the system provides a stable environment for applications.

For most spatial co-location pattern mining methods, distance threshold is used as a standard to measure the neighboring relation among instances of different objects, then to mine frequent co-location patterns, but the interation between instances with neighboring relations and the gain rate of patterns are not considered. In the spatial co-location patterns mining process, by introducing the interation rate between instances and the seasonal average income of objects, the concepts of object effect rate, suite total income and gain rate were defined, and a basic algorithm named NAGA and an efficient pruning algorithm named NAGA_JZ for mining high gain rate co-location patterns were put forward. Finally, a large number of experiments were carried out to verify the correctness and practicability of the basic algorithm, and the mining efficiency of the basic algorithm and the pruning algorithm were compared. The experimental results prove the high efficiency of the pruning algorithm.