Privacy Preserving in Data Mining (PPDM) is a process by which certain sensitive information is hidden during data mining without precise access to original dataset. Majority of the techniques proposed in the literature for hiding sensitive information are based on using Support and Confidence measures in the association rules, which suffer from limitations. In this paper we propose a novel architecture which acquired other standard statistical measures instead of conventional framework of Support and Confidence to generate association rules. Specifically a weighing mechanism based on central tendency is introduced. The proposed architecture is tested with UCI datasets to hide the sensitive association rules as experimental evaluation. A performance comparison is made between the new technique and the existing one. The new architecture generates no ghost rules with complete avoidance of failure in hiding sensitive association rules. We demonstrate that Support and Confidence are not the only measures in hiding sensitive association rules. This research is aimed to contribute to data mining areas where privacy preservation is a concern.