The Design and Implementation of a Data Level Database Inference Detection System

Inference is a way to subvert access control mechanisms of database systems. Most existing work on inference detection relies on analyzing functional dependencies in the database schema. This paper is an extension to our earlier e ort in developing a data level inference detection system [Yip and Levitt, 1998]. In this paper, we introduce the split query inference rule, make an extension to the overlapping inference rule, and provide an in depth discussion on the applications of the inference rules on union queries. Data level inference detection is inevitably expensive. We have developed a prototype of the inference detection system to evaluate its performance. The result shows that the system performs better with larger number of attributes and records in the database, and smaller number of projected attributes and return tuples of the queries. Therefore, the inference detection system could be practical when users retrieve a small amount of data compare to the size of the database.