SIVA: Security Intelligence Verification Algorithm for Location based Services

Abstract The basic objective of SIVA is to verify the security and privacy of location based information and communication services based on collective intelligence. The security intelligence of a location based services should be analyzed through a multi-dimensional view : (P1) topology in terms of number of sensors, relays, base stations, sub-stations and their connectivity in a smart grid; (P2) identity of the objects associated with the topology; (P3): position or location of the objects in terms of X, Y and Z coordinates, longitude, latitude, radius, zone and distance between the objects; (P4) movement of mobile objects in terms of speed or velocity, acceleration, direction, route map and distance from source and destination and (P5) collective intelligence in terms of workflow control pattern, resources allocation and their roles, collaborative intelligence, coordination and integration strategies. The research methodology is basically case based reasoning on a smart grid. The basic components of the smart grid are sensors, relays, wireless communication channel and base station; the application domains are sensor networks, mobile communication and SCADA system. The objective is to optimize system performance and security intelligence of the smart grid subject to a set of constraints such as cost of communication and quality of service. The topology of the smart grid consists of n sensor nodes and m relays with a specific range of communication. For instance, it is required to compute a Steiner tree interconnecting all nodes with minimum number of Steiner points such that the Euclidean length of each edge is no more than the given positive constant. Effective location based services requires efficient processing of access requests to find the past, present and future location of the mobile agents or objects. But, it raises several security and privacy concerns and demands a comprehensive security policy. Location based access control is important to preserve the privacy of the mobile objects or agents in terms of their identities, position, path movement and interaction. Traditional authentication and privacy protection techniques are not sufficient to ensure the security of a smart grid efficiently in a robust way. This work presents Security Intelligence Verification Algorithm (SIVA) for a smart grid based on threats analytics. It defines the security intelligence of the grid comprehensively with a novel concept of collective intelligence and location based access control mechanism. The basic objective is to search for the desired moving objects that satisfy the query and identify and enforce the relevant security policies. SIVA is analyzed from the perspectives of security intelligence, communication complexity and computational intelligence. The security intelligence of SIVA is defined in terms of location privacy: topology, identity, position, path, movement and interaction; authentication, authorization, correct identification, confidentiality and audit; fairness, correctness, transparency, accountability, trust, non-repudiation and data integrity; reliability, consistency, liveness, deadlock freeness, safety and reachability. The computational intelligence is associated with the complexity of Steiner tree or disc graph and location based access control policies such as obfuscation and anonymity algorithms. The cost of communication is a function of number of sensor nodes and relays in the smart grid and communication protocol. But, a complex security and privacy policy may incur computation and communication overhead and may degrade the performance of the grid. It is essential to organize the mobile objects, their profile and authorizations and serve access requests efficiently. SIVA verifies location privacy in terms of position based conditions on the location of the sensors and relays, movement based conditions on the mobility, interaction and information privacy of identity, position and path of the mobile objects. It also verifies the effectiveness of location privacy strategies in terms of anonymity, policy and obfuscation. Privacy should be enforced at different levels for different applications based on rational reasoning. Keywords: Location privacy, Security intelligence, Verification algorithm, Smart grid, Sensor networks, Mobile communication, Computational complexity, Steiner tree, Communication cost.