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.

随着全球定位系统、蜂窝网络和相关技术的快速发展，人们可以使用手持设备快速、轻松地向服务提供商请求基于位置的服务（LBS）。近年来，与LBS服务相关的隐私问题备受关注。本文重点研究基于密码学的k-最近邻查询难题。我们没有使用传统的 Hilbert 曲线来存储目标地图中兴趣点的信息，而是使用 Z-order 曲线和四叉树来存储位置信息。这样，它提高了本地化的效率，并且能够支持分布式存储。同时，该算法使用支持加法和乘法同态的同态加密算法ASM-PH来保密查询，在只有用户知道加密内容的情况下，通过LBS服务器或存储设备计算加密内容，可以节省用户的成本。实验结果表明，该算法具有较高的查询准确率和较低的用户加解密成本。同时，我们提出了一种支持该方案的分布式存储隐私查询方案。该方案基于分布式存储和二次残差定理，是一种安全高效的LBS用户隐私保护方案。我们提出了一种支持该方案的分布式存储隐私查询方案。该方案基于分布式存储和二次残差定理，是一种安全高效的LBS用户隐私保护方案。我们提出了一种支持该方案的分布式存储隐私查询方案。该方案基于分布式存储和二次残差定理，是一种安全高效的LBS用户隐私保护方案。