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Inference detection in statistical database using frequent pattern

机译:使用频繁模式在统计数据库中进行推理检测

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摘要

Statistical database (SDB) is widely used in commer-cial, business and multifarious domains of the like. Protecting the sensitive information in database is a critical issue with the rapid increase in usage of DBaaS (Data base as a service). Statistical database (SDB) contains sensitive information, so queries revealing data specific to a particular individual or event is not permitted. It only allows a user to perform aggregate statistical queries. At times, when a user executes a series of aggregate queries which helps the user draw enough statistics about a single individual, revealing the sensitive information. The main objective is to restrain the usage of the SDB such that only aggregate values are obtained. This paper proposes an algorithm to solve this issue using Frequent Pattern Mining technique. The proposed algorithm is validated and compared with the existing approaches.
机译:统计数据库(SDB)广泛用于商业,商业和类似领域。随着DBaaS(数据库即服务)的使用迅速增加,保护数据库中的敏感信息是一个关键问题。统计数据库(SDB)包含敏感信息,因此不允许显示特定于特定个人或事件的数据的查询。它仅允许用户执行汇总统计查询。有时,当用户执行一系列汇总查询时,这有助于用户绘制有关单个个人的足够统计信息,从而揭示敏感信息。主要目的是限制SDB的使用,以便仅获得合计值。本文提出了一种使用频繁模式挖掘技术解决此问题的算法。对该算法进行了验证,并与现有方法进行了比较。

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