In this paper, we consider a minimum distance Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation (control) of tabular data. The goal of the CTA model is to find the closest tabular data to the original data while ensuring that the disclosure risk is minimized. This is achieved by formulating the problem as a second-order cone program, which can be solved efficiently using convex optimization techniques. The proposed approach is demonstrated through numerical experiments, showing its effectiveness in preserving the confidentiality of sensitive information.