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Generating Private Synthetic Databases for Untrust
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Evaluating the performance of database systems iscrucial when database vendors or researchers are developingnew technologies. But such evaluation tasks rely heavily onactual data and query workloads that are often unavailable toresearchers due to privacy restrictions. To overcome this barrier,we propose a framework for the release of a synthetic databasewhich accurately models selected performance properties ofthe original database. We improve on prior work on syntheticdatabase generation by providing a formal, rigorous guarantee ofprivacy. Accuracy is achieved by generating synthetic data using acarefully selected set of statistical properties of the original datawhich balance privacy loss with relevance to the given queryworkload. An important contribution of our framework is anextension of standard differential privacy to multiple tables
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Geometric method of k-query
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Returning tuples that users may be interested in is one of the most important goals for multi-criteria decision making. Top-k queries and skyline queries are two representative queries. A top-k query has its merit of returning a limited number of tuples to users but requires users to give their exact utility functions. A skyline query has its merit that users do not need to give their exact utility functions but has no control over the number of tuples to be returned. In this paper, we study a k-regret query, a recently proposed query, which integrates the merits of the two representative queries. We first identify some interesting geometry properties for the k-regret query. Based on these properties, we define a set of candidate points called happy points for the k-regret query, which has not been studied in the literature. This result is very fundamental and beneficial to not only all existing algorithms but also all new algorithms to be developed for the k-regret query. Since it is
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