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With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. Specifically, our paper aims to address the following problem: Given a user’s location s, a target destination t, and a graph G stored in a cloud, can users retrieve the shortest path route from s to t in a privacy-preserving manner? Although there exist a few solutions to this problem, they are either inefficient or insecure. For example, existing solutions either leak intermediate results to untrusted cloud providers or incur significant costs on the end-user. To address this gap, we propose an efficient and secure solution based on homomorphic encryption properties combined with a novel data aggregation technique. We formally show that our solution achieves semantic security guarantees under the semi-honest model. Additionally, we provide complexity analysis and experimental results to demonstrate that the proposed protocol is significantly more efficient than the current state-of-the-art techniques.

ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services