Privacy-preserving data publishing has been widely explored in academia recently. The state-of-the-art goal for data privacy-preserving is differential privacy, which offers a strong degree of privacy protection against adversaries with arbitrary background knowledge. However, along with a wide query scope in the non-interactive model like DiffGen, the accumulation of noise in the query answers ... [Show full abstract] can affect the usability of the released data. In this paper, we present Consistent DiffGen(CDiffGen), a non-interactive differentially-private algorithm. It aims at range-count queries and optimises the DiffGen module with the consistency constraints among data attributes. We experimentally evaluate CDiffGen on real dataset and the result performs the effectiveness and the improvement of our solution in range-count query tasks.