In privacy-preserving geographical information systems, there is a constant trade-off between utility and privacy leakage of data. Adding noise drawn randomly from a probability density function to the location data sacrifices some utility to maintain privacy. Truncating these probability density functions ensures the noise added will always be within a predetermined range. This allows more control over the balance between the utility of the service and the privacy to be maintained. This project implements various mechanisms which truncate and redistribute this probability mass and studies their effect on the utility of the service. Results of experiments run through the implementation of these mechanisms are presented and discussed.