We address the problem of replicating a Voronoi diagram @math of a planar point set @math by making proximity queries, which are of three possible (in decreasing order of information content): 1. the exact location of the nearest site(s) in @math ; 2. the distance to and label(s) of the nearest site(s) in @math ; 3. a unique label for every nearest site in @math . We provide algorithms showing how queries of Type 1 and Type 2 allow an exact cloning of @math with @math queries and @math processing time. We also prove that queries of Type 3 can never exactly clone @math , but we show that with @math queries we can construct an @math -approximate cloning of @math . In addition to showing the limits of nearest-neighbor database security, our methods also provide one of the first natural algorithmic applications of retroactive data structures.
Bancilhon and Spyratos @cite_21 , Deutsch and Papakonstantinou @cite_42 , and Miklau and Suciu @cite_14 provide general models for privacy loss in information releases from a database, called , which identifies the sensitive information as a specific secret, @math . Attackers are allowed to form legal queries and ask them of the database, while the database owner tries to protect the information that these queries leak about the secret @math . Note that we are instead considering a related, but different, quantitative measure, where there is no specifically sensitive part of the data, but the data owner, Alice, is trying to limit releasing too much of her data.
@cite_14 @cite_21 @cite_42
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We perform a theoretical study of the following query-view security problem: given a view V to be published, does V logically disclose information about a confidential query S? The problem is motivated by the need to manage the risk of unintended information disclosure in today's world of universal data exchange. We present a novel information-theoretic standard for query-view security. This criterion can be used to provide a precise analysis of information disclosure for a host of data exchange scenarios, including multi-party collusion and the use of outside knowledge by an adversary trying to learn privileged facts about the database. We prove a number of theoretical results for deciding security according to this standard. We also generalize our security criterion to account for prior knowledge a user or adversary may possess, and introduce techniques for measuring the magnitude of partial disclosures. We believe these results can be a foundation for practical efforts to secure data exchange frameworks, and also illuminate a nice interaction between logic and probability theory. This paper is concerned with protection of information in relational databases from disclosure to properly identified users. It is assumed that the only means of access to the database is through a relational query language. The objective of the paper is to formalize the notion of protection. We first describe the information content of the database by a set of propositions and their truth values. The objects to be protected are (the truth values of) certain propositions that have been declared confidential. A query violates a protected proposition if its answer modifies the knowledge of tHe user about (the truth value of) this proposition. Following this approach, we propose a model for evaluating protection systems. In this model a protection system is characterized by the type of queries it takes as its input, the type of data it can protect, the means of protection against queries (e.g. rejection or modification) and the type of protection it provides (e.g., total protection, partial protection, protection against user's inference). Some examples of the use of the model as a tool for analysis are given. We formulate and study a privacy guarantee to data owners, who share information with clients by publishing views of a proprietary database. The owner identi.es the sensitive proprietary data using a secret query against the proprietary database. Given an extra view, the privacy guarantee ensures that potential attackers will not learn any information about the secret that could not already be obtained from the existing views. We de.ne “learning” as the modi.cation of the attacker's a-priori probability distribution on the set of possible secrets. We assume arbitrary a-priori distributions (including distributions that correlate the existence of particular tuples) and solve the problem when secret and views are expressed as unions of conjunctive queries with non-equalities, under integrity constraints. We consider guarantees (a) for given view extents (b) for given domain of the secret and (c) independent of the domain and extents.