Location Based Service (LBS) is one of the most popular mobile services today, which offers a wide range of location-based services information about the physical location of a user or device. Typical LBS includes real-time step-by-step directions, searching for points of interest (POI) or social networking services such as Facebook, Foursquare, Loopt and Qype. However, user location privacy is a major concern in today's mobile applications and significant research has been devoted to this issue. Various Location Privacy Preserving Mechanisms (LPPMs) have been used to preserve the privacy of mobile users' location information. This survey aims to present the privacy preservation mechanisms used in location-based services. Additionally, LPPMs are classified into cryptographic and non-cryptographic mechanisms, and the taxonomy of the mechanisms is also discussed. Furthermore, the classification and comparison of different mechanisms are presented based on the probability distribution attack. The strengths and weaknesses of the different mechanisms are also highlighted. The development of new technologies has profoundly influenced traditional mobile networks that use users' location information for data transmission. However, recently, user location information has been widely used for location-based services (LBS). In mobile networks, privacy is the main concern because it does not have a clear line of protection. Location-based services refer to a wide range of services based on information about the physical location of a user or device. Typical LBS includes social networking services such as Facebook ~cite{Facebook:2014:fb} or Foursquare ~cite{Foursquare:2014:fs}, where the user shares their...... middle of paper... ...Based on the objectives of defense mechanisms and attackers' knowledge, the classification of privacy-preserving mechanisms used in location-based services will be presented in this section, as shown in Figure ef{fig :classification}. In summary, one of the problems with obfuscation mechanisms is that the effective size of the targeted obfuscation zone can be minimized if an adversary applies certain prior knowledge (map knowledge). The adversary can derive an attack function by probability distribution of the user's actual location on the obfuscation zone. If the probability is not uniformly distributed (i.e. the distribution is more concentrated around the user's real location), an adversary can easily discover the user's real location with a high probability. Therefore, privacy depends on the adversary and neglecting the adversary's knowledge and capabilities limits privacy protection..