7 research outputs found
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Causality has been recently introduced in databases, to model, characterize
and possibly compute causes for query results (answers). Connections between
query causality and consistency-based diagnosis and database repairs (wrt.
integrity constrain violations) have been established in the literature. In
this work we establish connections between query causality and abductive
diagnosis and the view-update problem. The unveiled relationships allow us to
obtain new complexity results for query causality -the main focus of our work-
and also for the two other areas.Comment: To appear in Proc. UAI Causal Inference Workshop, 2015. One example
was fixe
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
In this work we establish and investigate connections between causes for
query answers in databases, database repairs wrt. denial constraints, and
consistency-based diagnosis. The first two are relatively new research areas in
databases, and the third one is an established subject in knowledge
representation. We show how to obtain database repairs from causes, and the
other way around. Causality problems are formulated as diagnosis problems, and
the diagnoses provide causes and their responsibilities. The vast body of
research on database repairs can be applied to the newer problems of computing
actual causes for query answers and their responsibilities. These connections,
which are interesting per se, allow us, after a transition -inspired by
consistency-based diagnosis- to computational problems on hitting sets and
vertex covers in hypergraphs, to obtain several new algorithmic and complexity
results for database causality.Comment: To appear in Theory of Computing Systems. By invitation to special
issue with extended papers from ICDT 2015 (paper arXiv:1412.4311
Characterizing and computing causes for query answers in databases from database repairs and repair programs
A correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints has already been established. In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes. Here, causes are also introduced at the attribute level by appealing to a both null-based and attribute-based repair semantics. The corresponding repair programs are presented, and they are used as a basis for computation and reasoning about attribute-level causes
Achieving data privacy through secrecy views and null-based virtual updates
We may want to keep sensitive information in a relational database hidden from a user or group thereof. We characterize sensitive data as the extensions of secrecy views. The database, before returning the answers to a query posed by a restricted user, is updated to make the secrecy views empty or a single tuple with null values. Then, a query about any of those views returns no meaningful information. Since the database is not supposed to be physically changed for this purpose, the updates are only virtual, and also minimal. Minimality makes sure that query answers, while being privacy preserving, are also maximally informative. The virtual updates are based on null values as used in the SQL standard. We provide the semantics of secrecy views, virtual updates, and secret answers (SAs) to queries. The different instances resulting from the virtually updates are specified as the models of a logic program with stable model semantics, which becomes the basis for computation of the SAs