Generating Private Synthetic Databases for Untrust
2016-08-23
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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
python
系统
数据库
生成
综合
评价
信任
私人
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