Currently, more and more mobile terminals embed a number of sensors and generate massive data. Effective utilization to such information can enable people to get more personalized services, and also help service providers to sell their products accurately. As the information may contain privacy information of people, they are typically encrypted before transmitted to the service providers. This, however, significantly limits the usability of data due to the difficulty of searching over the encrypted data. To address the above issues, in this paper, we first leverage the secure kNN technique to propose an efficient and privacy-preserving multi-feature search scheme for mobile sensing. Furthermore, we propose an extended scheme, which can personalize query based on the historical search information and return more accurate result. Using analysis, we prove the security of the proposed scheme on privacy protection of index and trapdoor and unlinkability of trapdoor. Via extensive experiment on real-world cloud systems, we validate the performance of the proposed scheme in terms of functionalities, computation and communication overhead. 
Achieving efficient and privacy-preserving multi-feature search for mobile sensing