In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user’s privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change. In this research we proposed a strategy for location privacy protection which addresses the issues related with with location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance It has been revealed that changing pseudonym at improper time and location may threat to user’s privacy. Moreover, certain methods related to pseudonym change have been proposed to to favorite desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes . By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number (DPMM) technique, which ensures highest level of accuracy and privacy. of pseudonym change.