Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach
研究比较了保护数值数据库属性的不同安全机制，发现固定数据扰动方法在安全性和可访问性上表现最佳，其中乘法方法最符合要求。
Organizations store data regarding their operations, employees, consumers, and suppliers in their databases. Some of the data are considered confidential, and by law, the organization is required to provide appropriate security measures in order to preserve privacy. Yet a number of companies have little or no security measures. The reason for this lack of security may, at least in part, be attributed to a lack of awareness and empirical evidence about the relative effectiveness of security mechanisms. This study investigates the effectiveness of different security mechanisms for protecting numerical database attributes. The trade-off between security, accessibility, and accuracy are examined. A comparison of different security mechanisms reveals that fixed data perturbation is preferred because it maximizes both security and accessibility. An investigation of the different approaches to fixed data perturbation indicates that multiplicative method best meets these criteria.