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In privacy-preserving record linkage, a number of data custodians encode their records and submit them to a trusted third-party who is responsible for identifying those records that refer to the same real-world entity. In this paper, we propose FEDERAL, a novel record linkage framework that implements methods for anonymizing both string and numerical data values, which are typically present in data records. These methods rely on a strong theoretical foundation for rigorously specifying the dimensionality of the anonymization space, into which the original values are embedded, to provide accuracy and privacy guarantees under various models of privacy attacks. A key component of the applied embedding process is the threshold that is required by the distance computations, which we prove can be formally specified to guarantee accurate results. We evaluate our framework using three real-world data sets with varying characteristics. Our experimental findings show that FEDERAL offers a complete and effective solution for accurately identifying matching anonymized record pairs (with recall rates constantly above 93 percent) in large-scale privacy-preserving record linkage tasks. [ABSTRACT FROM PUBLISHER]

We present a $\Lambda$ -fold Redundant Blocking Framework, that relies on the Locality-Sensitive Hashing technique for identifying candidate record pairs, which have undergone an anonymization transformation. In this context, we demonstrate the usage and evaluate the performance of a variety of families of hash functions used for blocking. We illustrate that the performance attained is highly correlated to the distance-preserving properties of the anonymization format used. The parameters, of the blocking scheme, are optimally selected so that we achieve the highest possible accuracy in the least possible running time. We also introduce an SMC-based protocol in order to compare the formulated record pairs homomorphically, without running the risk of breaching the privacy of the underlying records. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent item sets by 1) minimally extending the original database by a synthetically generated database part—the database extension, 2) formulating the creation of the database extension as a constraint satisfaction problem, 3) mapping the constraint satisfaction problem to an equivalent binary integer programming problem, 4) exploiting underutilized synthetic transactions to proportionally increase the support of nonsensitive item sets, 5) minimally relaxing the constraint satisfaction problem to provide an approximate solution close to the optimal one when an ideal solution does not exist, and 6) using a partitioning in the universe of the items to increase the efficiency of the proposed hiding algorithm. Extending the original database for sensitive item set hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. Moreover, the application of binary integer programming enables the simultaneous hiding of the sensitive item sets and thus allows for the identification of globally optimal solutions. [ABSTRACT FROM AUTHOR]

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area. [ABSTRACT FROM AUTHOR]

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. In this paper, we investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database. [ABSTRACT FROM AUTHOR]