Efficient and Secure Spatial Fuzzy Keyword Query

With the popularity of location-based services (LBSs) and the explosive growth of spatial data, massive spatial data has been outsourced to cloud servers for storage and query services, such as spatial range and fuzzy keyword query. To protect data and user privacy, it is essential to encrypt the data and query requests before uploading them. However, encrypting the data makes search on ciphertext more challenging. Moreover, the existing schemes do not support spatial range and fuzzy keyword query (SFKQ for short) simultaneously. To this end, we first leverage the indecomposable property of primes and the Geohash algorithm to create an index vector and query trapdoor vector. We then propose concrete constructions for SFKQ, which support both spatial range query and fuzzy multikeyword query in a single interaction. Additionally, we carefully design a novel Geohash-based index structure (referred to as GeoTree) along with a pruning strategy to accelerate search efficiency. Finally, we provide the formal security analysis to demonstrate that the proposed SFKQ scheme is indistinguishable under chosen-plaintext attacks (IND-CPA), and we conduct comprehensive experiments to validate the accuracy and efficiency.