Location-based service (LBS) has recently become popular in almost all social and business fields due to the boom of location-aware mobile electronic devices. LBS, albeit providing enormous benefits to individuals and society, poses a serious threat to users' privacy as they are enticed to disclose their locations and query attributes to untrusted LBS providers via their LBS queries. Moreover, the contextual information attached to these locations and service attributes can reveal users' personal interests, life styles, health conditions, etc. How to preserve users' privacy against potentially malicious LBS providers is of vital importance to the well-being of LBS ecosystem, and as such, it attracts great attentions from many researchers. This paper provides a review of the state-of-the-art of privacy preserving for LBS. First, the concept and threat model of LBS privacy are presented. Then, the existing schemes for preserving users' LBS privacy are described in detail from the aspects of architecture, metric and technology. Next, a pointed discussion is placed on the latest mainstream technology, with emphasis on the distortion-based technology. Further, following a comprehensive comparison and analysis of the performance and defects of various technologies, the problems and possible solutions for LBS privacy preserving are pointed out. Finally, some future research directions are provided.