As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from the practice in terms of efficiency and scalability. In this paper, we propose a novel solution based on Geohash and predicate symmetric searchable encryption for secure range queries named as MixGeo. We present a multi-level indexes structure tailored for efficient and large-scale spatial data lookup in the cloud server while preserving data privacy.

Our experiment on a real-world spatial dataset in the cloud environment shows that it only takes less than 0.1s for once update operation over 65,128 encrypted dense data and is over 100 times faster than the existing solutions.