An Efficient and Secure Shortest-Path Discovery in Location-Based Services

Cloud computing is a rapidly growing technology that offers a cost-efficient infrastructure with on-demand scalability and increased flexibility. Security and Privacy has been the biggest hurdle for the widespread cloud migration. Specifically, the data stored in cloud carries a lot of user’s sensitive information and is vulnerable to attacks because of reduced visibility and control. A commonly used data structure in computing is Graph, which is flexible and dynamic. Graphs are ubiquitous and are becoming more pervasive across various domains. One such domain is military, where the graph theory is employed to determine the organization structure and network flow which aids in transporting the supplies for optimal usage of resources while attacking the target. In our project, we focus on Location-Based Services, where users want to retrieve the services like map directions without disclosing their locations. In particular, our research aims to address the following problem: Given a user’s location S and a preferred destination T over a graph G, can we retrieve the shortest path route from S to T in a privacy-preserving manner? Although, there exist some solutions for this problem, they are either inefficient or insecure. To address this gap, we proposed an efficient and secure solution based on homomorphic encryption with novel data aggregation technique. Additionally, we investigate the security and complexity of our solution and present a comparative performance analysis to demonstrate the superiority of the proposed solution compared to the existing work.