We consider the problems of finding and determining certain query answers and of determining containment between queries; each problem is formulated in presence of materialized views and dependencies under the closed-world assumption. We show a tight relationship between the problems in this setting. Further, we introduce algorithms for solving each problem for those inputs where all the queries and views are conjunctive, and the dependencies are embedded weakly acyclic. We also determine the complexity of each problem under the security-relevant complexity measure introduced by Zhang and Mendelzon in 2005. The problems studied in this paper are fundamental in ensuring correct specification of database access-control policies, in particular in case of fine-grained access control. Our approaches can also be applied in the areas of inference control, secure data publishing, and database auditing.
CWA & dependencies: problem of finding all certain query answers: this problem was introduced (with dependencies) under OWA in @cite_24 @cite_25 this problem was solved for CWA OWA dependency free in @cite_9 (full total dependencies @cite_12 suggested as extension)
@cite_24 @cite_9 @cite_25 @cite_12
1526397503 2160983447 2152318637 1569370144
In relational database systems a combination of privileges and views is employed to limit a user's access and to hide non-public data. The data privacy problem is to decide whether the views leak information about the underlying database instance. Or, to put it more formally, the question is whether there are certain answers of a database query with respect to the given view instance. In order to answer the problem of provable date privacy, we will make use of query answering techniques for data exchange. We also investigate the impact of database dependencies on the privacy problem. An example about health care statistics in Switzerland shows that we also have to consider dependencies which are inherent in the semantics of the data. We study the complexity of the problem of answering queries using materialized views. This problem has attracted a lot of attention recently because of its relevance in data integration. Previous work considered only conjunctive view definitions. We examine the consequences of allowing more expressive view definition languages. The languages we consider for view definitions and user queries are: conjunctive queries with inequality, positive queries, datalog, and first-order logic. We show that the complexity of the problem depends on whether views are assumed to store all the tuples that satisfy the view definition, or only a subset of it. Finally, we apply the results to the view consistency and view self-maintainability problems which arise in data warehousing. Existing work on inference detection for database systems mainly employ functional dependencies in the database schema to detect inferences. It has been noticed that analyzing the data stored in the database may help to detect more inferences. We describe our effort in developing a data level inference detection system. We have identified five inference rules that a user can use to perform inferences. They are "subsume", "unique characteristic", "overlapping", "complementary", and "functional dependency" inference rules. The existence of these inference rules confirms the inadequacy of detecting inferences using just functional dependencies. The rules can be applied any number of times and in any order. These inference rules are sound. They are not necessarily complete, although we have no example that demonstrates incompleteness. We employ a rule based approach so that future inference rules can be incorporated into the detection system. We have developed a prototype of the inference detection system using Perl on a Sun SPARC 20 workstation. The preliminary results show that on average it takes seconds to process a query for a database with thousands of records. Thus, our approach to inference detection is best performed offline, and would be most useful to detect subtle inference attacks. In this paper we study the implication and the finite implication problems for data dependencies. When all dependencies are total the problems are equivalent and solvable but are NP-hard, i.e., probably computationally intractable. For non-total dependencies the implication problem is unsolvable, and the finite implication problem is not even partially solvable. Thus, there can be no formal system for finite implication. The meta decision problems of deciding for a given class of dependencies whether the implication problem is solvable or whether implication is equivalent to finite implication are also unsolvable.