An Efficient and Secure Spatial Keyword Ciphertext Retrieval Scheme Based on Cloud-Fog Collaboration

Location-Based Services are increasingly common in our lives. In order to reduce user overhead, the data owner stores the location information and text data on the cloud server, and the user completes the retrieval task with the help of the fog server. To protect the privacy of outsourced data, many secure spatial keyword retrieval schemes have been proposed. Most schemes use R-tree indexes to improve the efficiency of ciphertext retrieval, but the encrypted R-tree index is hard to update. Moreover, some indexes based on order-preserving encryption are vulnerable to frequency-revealing attacks. So how to balance efficiency and security is a problem. To solve the above problems, we propose an efficient and secure spatial keyword ciphertext retrieval scheme based on cloud-fog collaboration. First, we innovatively design the SK-tree. The Geohash algorithm and Simhash algorithm are used in SK-tree to compress information, achieving efficient retrieval. Secondly, our retrieval tree has the function of fuzzy order preservation, which can better hide the correspondence between plaintext and ciphertext compared to traditional index-based order-preserving encryption schemes. In addition, we design a cloud-fog-user interaction scheme for attribute-based encryption that can hide access control policies, which reduces the computational overhead for the user side. Finally, we prove through theoretical analysis that our scheme ensures cloud data security and query trap information privacy. We compare our scheme with others through simulation experiments to demonstrate its superiority in efficiency.