Recent research shows that Data is one of the most valuable and important assets of organizations and businesses. Privacy in the dissemination of Data is becoming increasingly challenging. Anonymity as one of the privacy strategies, on the one side, conceals the relationship between individuals and records in a metadata table and, on the other side, preserves the usefulness of the data for subsequent analysis. Preventing information disclosure becomes difficult when the adversary possesses background knowledge. We propose an anonymization framework to protect against background knowledge attack, identity disclosure, and feature disclosure. The anonymization algorithm creates equivalence classes of records whose probability distributions extracted by background knowledge are similar. Our proposed algorithm satisfies k-anonymity and its extension too. The proposed anonymity algorithm tries to satisfy the privacy model while preserving the usefulness of the anonymous data. We verify the theoretical study by experimentation on two datasets. Experimental results show that our proposed algorithm outperforms the state of the art anonymization approaches in terms of loss of information.