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.
There has been considerable recent work on designing technological approaches that can help protect the privacy or intellectual property rights of a database by modifying its content. For example, several researchers (e.g., see @cite_0 @cite_16 @cite_7 @cite_0 @cite_24 @cite_33 @cite_11 @cite_31 ) have focused on data watermarking, which is a technique for altering the data to make it easier, after the fact, to track when someone has stolen information and published their own database from it. Alternatively, several other researchers @cite_32 @cite_23 @cite_39 @cite_17 @cite_2 @cite_10 @cite_37 @cite_28 have proposed as a way of specifying a quantifiable secrecy-preservation requirement for databases. The generalization approach is to group attribute values into equivalence classes, and replace each individual attribute value with its class name, thereby limiting the information that can be derived from any selection query. Our assumption in this paper, however, is that the data owner, Alice, is not interested in modifying her data, since she derives an economic interest from its accuracy. She may be interested instead in placing a reasonable limit on the number of queries any one user might ask, so that she can limit her exposure to the risk of that user cloning her data.
@cite_37 @cite_33 @cite_7 @cite_28 @cite_32 @cite_17 @cite_39 @cite_0 @cite_24 @cite_23 @cite_2 @cite_31 @cite_16 @cite_10 @cite_11
1551374365 1570343904 2136114025 128781889
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 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. The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We choose to use the earth mover distance measure for our t-closeness requirement. We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments. 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.