The importance of data-driven optimization in society and industry is increasing, including the utilization of data in enterprises and public institutions, as well as the advancement of Evidence-Based Policy Making (EBPM). However, conventional privacy protection technologies are becoming obsolete due to the evolution of attack techniques, making robust countermeasures an urgent necessity. To address this challenge, differential privacy (DP), a framework for protecting privacy that provides mathematical security against arbitrary attacks, has gained significant attention. In this paper, we focus on Zero-Concentrated Differential Privacy (zCDP), an extension of DP that incorporates Rènyi divergence into the security definition, and theoretically and experimentally evaluate the improvement of privacy protection for large-scale high-dimensional data. Specifically, we redefine the existing Non-Negative Wavelet (NN-Wavelet) method within the zCDP framework by replacing the Laplace mechanism with the Gaussian mechanism, and compare its performance with that of existing methods using population statistics based on census data. The evaluation results showed that the proposed method showed the highest accuracy for specific parameter settings. The composability of the proposed method in maintaining high accuracy across multiple data releases makes it especially effective for datasets such as demographic information, which are frequently updated and shared.