The cell suppression problem (CSP) is one of the most widely applied methods for tabular data protection. Given a set of primary cells to be protected, CSP aims at finding a set of secondary cells to be additionally removed to guarantee that estimates of values of primary cells fall out of a predefined protection interval. From a computational point of view, CSP is very challenging even for tables of moderate size and number of primary cells. Currently, the only effective optimal approach for CSP is Benders decomposition (also known as cutting planes). However, the convergence to the optimal solution is often too slow due to well known instability issues of Benders decomposition. This work discusses a recently developed improved Benders method, which focus on finding new solutions in the neighborhood of ”good” points. Some results are reported in the solution of realistic and real-world CSP instances, showing the effectiveness of this approach

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