In crowd-sourced data collection for statistical aggregate, Local Differential Privacy (LDP) has become the de facto mechanism for preserving privacy. The current LDP mechanisms primarily focus on enforcing a preset privacy-level for all participants. Other user driven mechanisms that attempt to provide participants with the freedom to choose privacy-levels, such as user-driven LDP (UD-LDP) and personalised LDP (PLDP), are limited, in achieving their goals. UD-LDP allows users to choose a privacy-level from a fixed set of values determined by the data collector while PLDP allows users to adjust privacy within their multidimensional data, but not their overall privacy-level. In this study we present a fully user-driven local differential privacy mechanism denoted as FUD-LDP which gives participants the freedom to choose their preferred privacy-level. At the same time, this method enhances the accuracy of the measured statistics. We also analyse and present the effects of various privacy-level distributions on the efficiency of FUD-LDP compared to other existing user driven mechanisms.