In response to queries asked to a statistical database, the query system should avoid releasing summary statistics that could lead to the disclosure of confidential individual data. Attacks to the security of a statistical database may be direct or indirect and, in order to repel them, the query system should audit queries by controlling the amount of information released by their responses. This paper focuses on sum-queries with a response variable of nonnegative real type and proposes a compact representation of answered sum-queries, called an information model in “normal form,” which allows the query system to decide whether the value of a new sum-query can or cannot be safely answered. If it cannot, then the query system will issue the range of feasible values of the new sum-query consistent with previously answered sum-queries. Both the management of the information model and the answering procedure require solving linear-programming problems and, since standard linear-programming algorithms are not polynomially bounded (despite their good performances in practice), effective procedures that make a parsimonious use of them are stated for the general case. Moreover, in the special case that the information model is “graphical.” It is shown that the answering procedure can be implemented in polynomial time.

An implementation of the auditing strategy is presented to avoid both exact and approximate disclosure. The key data structure is a query map, which is a graphical summary of answered queries. Since the size of a query map may be exponential in the number of answered queries, a query-restriction criterion is introduced to make every query map a graph. An auditing procedure on such a graph is presented and the computational issues connected with its implementation are discussed. All the computational tasks can be carried out efficiently but one, which is a provably intractable problem.

Protecting sensitive information in a two-dimensional table asked for by a statistical user of a database raises computational problems involving both the query system which should guarantee the data security, and the user who should be able to disclose sensitive data when it is unprotected. We provide a quadratic algorithm which allows the query system to test a censored table for security, and a linear algorithm to find a minimum number of suppressions sufficient for protecting all sensitive cells; however, if sensitive information refers not only to single cells but also to cell sets, we prove that the problem of minimizing the number of suppressions is NP-hard. Finally, we provide a cubic algorithm which allows a user to disclose sensitive information in a censored table.

Abstract A new class of chordal graphs, containing the class of strongly chordal graphs, is introduced. It is shown that these graphs, called doubly chordal graphs, are related to acyclic hypergraphs and are recognizable in polynomial time. Furthermore, after proving that the Steiner tree and the connected domination problems are polynomially solvable for doubly chordal graphs, it is shown that both problems are NP‐hard for a class of chordal graphs (called Helly chordal graphs) containing the class of doubly chordal graphs. © 1993 John Wiley & Sons, Inc.

We propose a method to protect sensitive information in a two-dimensional statistical table based on the suppression of certain marginal cells.A sensitive cell set is considered unprotected if its exact value can be computed from the values of nonsensitive cells and unsuppressed marginal cells.We provide efficient algorithms to solve the following problems: deciding whether the sensitive cell sets are protected, identifying and evaluating all unprotected cell sets, suppressing the fewest marginal cells to protect all the sensitive cells.

The evaluability of queries on a statistical database containing joinable tables connected by an intersection hypergraph is considered. A characterization of evaluable queries is given, which allows one to define polynomial-time procedures both for testing evaluability and for evaluating queries. These results are useful in designing an 'informed query system' for statistical databases which promotes an integrated use of stored information. Such a query system allows the user to formulate a query involving attributes from several joinable tables as if they were all contained in a single universal table.>

Distance-hereditary graphs have been introduced by Howorka and studied in the literature with respect to their metric properties. In this paper several equivalent characterizations of these graphs are given: in terms of existence of particular kinds of vertices (isolated, leaves, twins) and in terms of properties of connections, separators, and hangings. Distance-hereditary graphs are then studied from the algorithmic viewpoint: simple recognition algorithms are given and it is shown that the problems of finding cardinality Steiner trees and connected dominating sets are polynomially solvable in a distance-hereditary graph.

A new dynamic approach to the problem of determining the correct interpretation of a logically independent query to a relational database is described. The proposed disambiguating process is based on a simple user-system dialogue that consists in a sequence of decisions about the relevance (or not) of an attribute with respect to the user interpretation

Article Free Access Share on Chordality properties on graphs and minimal conceptual connections in semantic data models Authors: Giorgio Ausiello View Profile , Alessandro D'Atri View Profile , Marina Moscarini View Profile Authors Info & Claims PODS '85: Proceedings of the fourth ACM SIGACT-SIGMOD symposium on Principles of database systemsMarch 1985 Pages 164–170https://doi.org/10.1145/325405.325426Online:25 March 1985Publication History 0citation73DownloadsMetricsTotal Citations0Total Downloads73Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The acyclicity degree of a relational database scheme is an interesting topic due to several desirable properties of the corresponding database [5,16]. In this paper a simple and homogeneous way to characterize the acyclicity degree of a database scheme is given. The method is based on the existence in all acyclic database schemes of a nonempty set of relation schemes that satisfy a "pruning predicate", which is a property similar to the property satisfied by the leaves in an ordinary tree. This fact implies that such relation schemes may be eliminated using a recursive pruning algorithm in order to determine the acyclicity degree. Furthermore, if we use an incremental step by step design methodology, enriching the scheme one relation at a time, the pruning predicate suggests a set of properties that have to be verified by the new relation scheme in order to preserve the acyclicity degree of the database scheme.

This paper concerns query answering in relational databases. We assume a universe U of attributes and a set of values associated with each attribute. A database scheme is a given collection R = {1, 2, ..., n} of subsets of U, called relation schemes. A query in R is any subset of U. We call "context" any joinable subset of R. An unambiguous context is one in which a query receives the same answer independently of the subcontext used for the computation. A formal treatment of unambiguous contexts is presented and it is shown that they provide a suitable basis for discussing the universal relation assumption, the relationship uniqueness assumption, maximal objects, and other related concepts.