Update handling techniques in statistical databases

Security of update operations in statistical databases (SDB) is investigated. The Data Abstraction model is used as a conceptual model of SDB. Four different update handling models that use output and/or data perturbation to process updates in SDB are analyzed for the degree of protection that they provide and for the restrictions that they introduce to enforce security. The first model processes updates in tuples and is secure if the DBA is completely informed of users' protected data knowledge. The second model adds rounding as an output perturbation to the first model, and compromise is possible when users know a single protected data without the knowledge of the DBA. The necessary and sufficient condition for this compromise is given. However, the probability that this condition is satisfied is shown to be very small. The third model uses data perturbation to process each update without any delay, and the introduced error can be controlled. The last model combines previous models by using output and-data perturbation, and it is shown that the expected value of the error introduced due to perturbation is zero, and the variance of the error can be controlled.