Efficient Enforcement of Security Policies Based on Tracking of Mobile Users

Abstract

AbstractRecent advances to mobile communication, Global Positioning System (GPS) and Radio Frequency Identification (RFID) technologies have propelled the growth of a number of mobile services. These require maintaining mobile object’s location information and efficiently serving access requests on the past, present and future status of the moving objects. Moreover, these services raise a number of security and privacy challenges. To address this, security policies are specified to ensure controlled access to the mobile user’s location and movement trajectories, their profile information, and stationary resources based on the mobile user’s spatiotemporal information. Considering the basic authorization specification 〈subject, object, privilege 〉, in a mobile environment, a moving object can be a subject, an object, or both. Serving an access request requires to search for the desired moving objects that satisfy the query, as well as enforce the security policies.Often, enforcing security incurs overhead, and as a result may degrade the performance of a system. To alleviate this problem, recently Atluri and Guo have proposed an unified index structure, STPR-tree, to organize both the moving objects and authorizations specified over them. However, the STPR-tree is not capable supporting security policies based on tracking of mobile users. In this paper, we present an index structure, called SPPF-tree, which maintains past, present and future positions of the moving objects along with authorizations by employing partial persistent storage. We demonstrate how the SPPF-tree can be constructed and maintained, and provide algorithms to process two types of access requests, including moving object requests by stationary subjects such as locate and track, and stationary object requests by moving subjects.KeywordsLeaf NodeMobile UserIndex StructureSecurity PolicyAccess Control PolicyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.