This paper discusses security-control methods for statistical databases (SDBs) to prevent disclosure of confidential information. Four general approaches are identified: conceptual, query restriction, data perturbation, and output perturbation. The paper evaluates these methods based on criteria such as security, robustness, suitability for numerical/categorical attributes, dynamic SDBs, information richness, bias, precision, consistency, and cost. It highlights that no single method prevents both exact and partial disclosure, but some perturbation-based methods prevent exact disclosure and enable statistical disclosure control. However, these methods may introduce bias or suffer from the 0/1 query-set-size problem. The paper recommends future research to develop methods that prevent exact disclosure, provide statistical disclosure control, and avoid bias and the 0/1 problem. It also suggests improving bias-correction mechanisms and addressing small query-set-size issues. The paper discusses various SDB types, including online/offline, static/dynamic, centralized/decentralized, and dedicated/shared systems. It evaluates the performance of different security-control methods, including query-set-size control, query-set-overlap control, auditing, cell suppression, and partitioning. The paper concludes that while auditing is effective for small SDBs, it is impractical for larger ones due to high computational and storage costs. The study emphasizes the need for further research to enhance security-control methods for SDBs.This paper discusses security-control methods for statistical databases (SDBs) to prevent disclosure of confidential information. Four general approaches are identified: conceptual, query restriction, data perturbation, and output perturbation. The paper evaluates these methods based on criteria such as security, robustness, suitability for numerical/categorical attributes, dynamic SDBs, information richness, bias, precision, consistency, and cost. It highlights that no single method prevents both exact and partial disclosure, but some perturbation-based methods prevent exact disclosure and enable statistical disclosure control. However, these methods may introduce bias or suffer from the 0/1 query-set-size problem. The paper recommends future research to develop methods that prevent exact disclosure, provide statistical disclosure control, and avoid bias and the 0/1 problem. It also suggests improving bias-correction mechanisms and addressing small query-set-size issues. The paper discusses various SDB types, including online/offline, static/dynamic, centralized/decentralized, and dedicated/shared systems. It evaluates the performance of different security-control methods, including query-set-size control, query-set-overlap control, auditing, cell suppression, and partitioning. The paper concludes that while auditing is effective for small SDBs, it is impractical for larger ones due to high computational and storage costs. The study emphasizes the need for further research to enhance security-control methods for SDBs.