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.
As observed in , to the best of our knowledge, the formal problem that we address in this current paper has not been considered in the open literature. (Abiteboul and Duschka in @cite_9 consider the special case where the set of dependencies is empty, apply in their analysis a different type of complexity metric than we do in this paper, and do not provide algorithms alongside their complexity results.) The work @cite_26 addresses a problem that is similar to ours at the informal level; see for a detailed discussion of @cite_26 . Generally, the literature on privacy-preserving query answering and data publishing is represented by work on data anonymization and on differential privacy; @cite_3 is a recent survey. Most of that work focuses on probabilistic inference of private information, while in this paper we focus on the possibilistic situation, where an adversary can deterministically derive sensitive information. Further, our model of sensitive information goes beyond associations between individuals and their private sensitive attributes.
@cite_9 @cite_26 @cite_3
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This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user. We consider the problem of answering queries from databases that may be incomplete. A database is incomplete if some tuples may be missing from some relations, and only a part of each relation is known to be complete. This problem arises in several contexts. For example, systems that provide access to multiple heterogeneous information sources often encounter incomplete sources. The question we address is to determine whether the answer to a specific given query is complete even when the database is incomplete. We present a novel sound and complete algorithm for the answer-completeness problem by relating it to the problem of independence of queries from updates. We also show an important case of the independence problem (and therefore ofthe answer-completeness problem) that can be decided in polynomial time, whereas the best known algorithm for this case is exponential. This case involves updates that are described using a conjunction of comparison predicates. We also describe an algorithm that determines whether the answer to the query is complete in the current state of the database. Finally, we show that our ‘treatment extends naturally to partiallyincorrect databases. Permission to copy without fee all or part of this material is granted provided that the copies aTe not made OT distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requirea a fee and or special permission from the Endowment. Proceedings of the 22nd VLDB Conference Mumbai(Bombay), India, 1996 The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. The problem of information disclosure has attracted much interest from the research community in recent years. When disclosing information, the challenge is to provide as much information as possible (optimality) while guaranteeing a desired safety property for privacy (such as l-diversity). A typical disclosure algorithm uses a sequence of disclosure schemas to output generalizations in the nonincreasing order of data utility; the algorithm releases the first generalization that satisfies the safety property. In this paper, we assert that the desired safety property cannot always be guaranteed if an adversary has the knowledge of the underlying disclosure algorithm. We propose a model for the additional information disclosed by an algorithm based on the definition of deterministic disclosure function (DDF), and provide definitions of p-safe and p-optimal DDFs. We give an analysis for the complexity to compute a p-optimal DDF. We show that deciding whether a DDF is p-optimal is an NP-hard problem, and only under specific conditions, we can solve the problem in polynomial time with respect to the size of the set of all possible database instances and the length of the disclosure generalization sequence. We then consider the problem of microdata disclosure and the safety condition of l-diversity. We relax the notion of p-optimality to weak p-optimality, and develop a weak p-optimal algorithm which is polynomial in the size of the original table and the length of the generalization sequence.