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

Abstract

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 effort in developing a data level inference detection system [13]. 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. INTRODUCTIONAn inference occurs when a user infers data that the user is not allowed to access. In multilevel secure database systems, early work on inference detection employs a graph to represent the functional dependencies among attributes in the database schema. An inference occurs if there exists two paths between two attributes (or composite attributes), and the two paths are labeled at different S. Jajodia (ed.), Database Security XII

A model-theoretic approach to data anonymity and inference control

“…This means that the system would not disclose Alice's job to anyone who is not authorized to view it. Dynamic (or "auditing") approaches [6,2,7,12,16,11], by contrast, attempt to detect and block potential inferences of sensitive information at query time. If no inference is detected, the regular answer to the query can be released.…”

Using sample size to limit exposure to data mining

“…Much of this has come from the database security community. [DH96,HD92,HDW95,HDW97,YL98] This work has emphasized the problem of strict inferences -learning rules that always hold. This is a particular problem in the realm of multi-level secure databases, and much of the work has concentrated on problems in this domain.…”

“…Recent work has extended this to capturing data-level, rather than schema-level, functional dependencies [YL98]. However, data mining provides what could be viewed as probabilistic inferences.…”

A Study of Inference Problems in Distributed Databases

“…To prevent database inference, non-sensitive data which is related to sensitive data must be examined and perhaps modified, thus requiring further data sanitization. The problem of preventing database inference in a standalone database is quite challenging and has recently been under intensive study from diverse aspects (e.g., [2,3,[5][6][7][8][9]11,13,[15][16][17][18][19]29,30]). Database inference in distributed databases is an area in which very little work has been done.…”