ROLQ-TEE: Revocable and Privacy-Preserving Optimal Location Query Based on Trusted Execution Environment

Abstract

With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine the optimal placement of a new facility from a set of candidate locations. However, location data are sensitive and cannot be shared with other enterprises, so privacy-preserving optimal location query becomes particularly important. Although some privacy-preserving works have been proposed, they still suffer from other challenges, such as irrevocable query permissions and high communication overhead. To overcome these challenges, we propose a revocable and privacy-preserving optimal location query scheme based on TEE (Trusted Execution Environment). We employ a basic hash structure within the TEE to compute the intersection data of both parties. We use the concept of reverse nearest neighbor (RNN) to assess the impact of candidates, and then select the optimal facility location. In addition, to implement the revocation of query permissions, we introduce a key refresh strategy that adopts identity and timestamp. We evaluate the performance of the proposed scheme using real datasets, and the experimental results indicate strong practicality.

Keywords

• TEE
• revocation
• privacy protection
• optimal location query
• RNN