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
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In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the entire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results. This paper presents a way to embed watermarks into 2D vectordata. The watermarking system provides a high capacity and is robust against the following attacks: polyline simplifications like the Douglas-Peucker algorithm [1], moving and cropping of data and addition of small amounts of random noise. The system is designed for adding information to digital maps. The attacks mentioned above can happen during the daily work with these maps, so the watermark will not be destroyed by working with the data. Nevertheless it is possible to destroy it on purpose by using other attacks. The information is embedded by changing the x y-coordinates of datapoints within the tolerance of the data. A block code is used to reconstruct missing data when not enough datapoints are available and a synchronization provides a way to detect the information even when the map is cropped. Abstract.We enunciate the need for watermarking database relations to deter data piracy, identify the characteristics of relational data that pose unique challenges for watermarking, and delineate desirable properties of a watermarking system for relational data. We then present an effective watermarking technique geared for relational data. This technique ensures that some bit positions of some of the attributes of some of the tuples contain specific values. The specific bit locations and values are algorithmically determined under the control of a secret key known only to the owner of the data. This bit pattern constitutes the watermark. Only if one has access to the secret key can the watermark be detected with high probability. Detecting the watermark requires access neither to the original data nor the watermark, and the watermark can be easily and efficiently maintained in the presence of insertions, updates, and deletions. Our analysis shows that the proposed technique is robust against various forms of malicious attacks as well as benign updates to the data. Using an implementation running on DB2, we also show that the algorithms perform well enough to be used in real-world applications. Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem. The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to delete from the relation. We also present a polynomial time algorithm for optimal k-anonymity that achieves an approximation ratio independent of the size of the database, when k is constant. In particular, it is a O(k log k)-approximation where the constant in the big-O is no more than 4, However, the runtime of the algorithm is exponential in k. A slightly more clever algorithm removes this condition, but is a O(k log m)-approximation, where m is the degree of the relation. We believe this algorithm could potentially be quite fast in practice. Watermarking allows robust and unobtrusive insertion of information in a digital document. During the last few years, techniques have been proposed for watermarking relational databases or Xml documents, where information insertion must preserve a specific measure on data (for example the mean and variance of numerical attributes). In this article we investigate the problem of watermarking databases or Xml while preserving a set of parametric queries in a specified language, up to an acceptable distortion. We first show that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or treelike structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking. Today's globally networked society places great demands on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal generalizations. Mechanisms for privacy assurances (e.g., queries over encrypted data) are essential to a viable and secure management solution for outsourced data. On a somewhat orthogonal dimension but equally important, we find the requirement to be able to assert and protect rights over such data. k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n2). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data. we introduce a solution for relational database content rights protection through watermarking. Rights protection for relational data is of ever-increasing interest, especially considering areas where sensitive, valuable content is to be outsourced. A good example is a data mining application, where data is sold in pieces to parties specialized in mining it. Different avenues are available, each with its own advantages and drawbacks. Enforcement by legal means is usually ineffective in preventing theft of copyrighted works, unless augmented by a digital counterpart, for example, watermarking. While being able to handle higher level semantic constraints, such as classification preservation, our solution also addresses important attacks, such as subset selection and random and linear data changes. We introduce wmdb., a proof-of-concept implementation and its application to real-life data, namely, in watermarking the outsourced Wal-Mart sales data that we have available at our institute.