Expantion of location-based services is indebted for increase and development of various portable devices which are equipped with a locating tool. These systems receive users' location-based queries and provide information according to their location. Since users to perform queries must provide their location information to the service providers, there is a possibility of tracking and violation of their location privacy. Therefore, there are a lot of ongoing research on the methods to protect location privacy without reducing the quality of services these services include nearest neighbor queries, group nearest neighbor queries, and in this thesis group nearest group queries. Research in this area is divided into two categories. Hands have to increase the efficiency of queries and handle other provide security solutions to users during these requests. Reviewing previous studies, we find that numerous researches have been conducted about location privacy in types of neighbor queries, but nothing related to group nearest group queries. In this study, at first we introduced a taxonomy of the field and then for the first time a method is developed to protect location privacy during the performance of group nearest group queries, we call it “FMGNG”. In addition, a method is also developed for the reduction of computational complexity of queries that named as “SMGNG”. Examinations show that location privacy in these queries are well protected and at the same time, group nearest group query performance is sped up and optimized. The proposed method is that each member of the group, declares its anonymity area to service provider. The provider then, processes queries concidering these areas and returns a set of responses which include real response. Finally, users will find real response using a private filter.We also developed an alternative method that use an algorithm that we call it kk’. In the latter method, Private filter will increase group nearest group queries performance considering the nature of the data. Since there is no previous work in this field, the results of experiments in the first method are compared with Second method the query processing costs in the proposed approaches were calculated and security analysis was conducted. The results of this assessment show that the above mentioned methods results in location privacy protection and improve the time of response to user. Keywords: Group nearest group queries, Group nearest neighbor queries, k'k algorithm, Location based services, location privacy, FMGNG, SMGNG