Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to train the prediction models in a centralised server. This amounts to considerably long waiting times before the model kicks in. Disclosing highly sensitive location information to third party entities also exposes the user to several privacy risks. To address these issues, we put forth a mobility prediction system, able to provide swift realtime predictions, evading the strenuous training procedure. We enable this by constantly adapting the model to substantive user mobility behaviours that facilitate accurate predictions even on marginal time bounded movements. In comparison to existing frameworks, we utilise less volumes of data to produce satisfactory prediction accuracies. This in turn lowers the computational complexity making implementation on mobile devices feasible and a step towards privacy preservation. Here, only the predicted location can be sent to such services to maintain the utility/privacy tradeoff. Our preliminary evaluations based on real world mobility traces corroborate our hypothesis.