Towards efficient privacy-preserving keyword search for outsourced data in intelligent transportation systems

Abstract

Privacy-preserving keyword search is important for outsourced data in Intelligent Transportation Systems (ITS). Traditional keyword search techniques utilized homomorphic encryption and searchable encryption to achieve privacy protection. However, the techniques generally suffer from high computational and communication costs, especially in high-security and large-scale data scenarios. To address this issue, this paper proposes an efficient privacy-preserving keyword search scheme for outsourced data in ITS. Firstly, by optimizing probabilistic homomorphic encryption to deterministic encryption, the computational cost on the data owner side is reduced and the ciphertext size is decreased, effectively reducing communication costs. Then, a secure comparison protocol and a secure inequality test algorithm are designed to achieve privacy-preserving keyword search, with enhanced privacy of the search results through the introduction of a random number scheme. The decryption operation for the end users is migrated to the cloud, further alleviating the computational and communication burden on the end users while ensuring system privacy. Finally, theoretical analysis and experimental results show that the proposed scheme outperforms existing methods in terms of computational efficiency and communication cost, making it particularly suitable for outsourced data scenarios in ITS.