K-Nearest Neighbor Privacy Protection Query for Distributed Storage in Location-based Service

With the rapid development of global positioning systems, cellular networks, and relative technologies, people can request location-based services (LBSs) from the service provider using a handheld device quickly and easily. In recent years, privacy issues related to LBS services have attracted a lot of attention. In this paper, we focus on the difficult problem of k-nearest neighbor query based on cryptography. Instead of using the traditional Hilbert curve to store the information of the points of interest in the target map, we use the Z-order curve and quad-tree to store location information. In this way, it improves the efficiency of localization and is able to support distributed storage. Meanwhile, the algorithm uses the homomorphic encryption algorithm ASM-PH that supports additive and multiplicative homomorphism to keep the query secret, which can save the user's cost by calculating the encrypted content with the LBS server or the storage devices when only the user knows the encrypted content. Experimental results show that the algorithm has high query accuracy and low user encryption and decryption cost. At the same time, we propose a privacy query scheme on distributed storage that supports the scheme. Based on distributed storage and quadratic residual theorem, this scheme is a secure and efficient LBS user privacy protection scheme.