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    • 2. 发明授权
    • Strategies for sanitizing data items
    • 消除数据项目的策略
    • US07509684B2
    • 2009-03-24
    • US10962223
    • 2004-10-09
    • Orville C. McDonaldXiaoan WangMarcelo M. De BarrosRichard K. BonillaQi Ke
    • Orville C. McDonaldXiaoan WangMarcelo M. De BarrosRichard K. BonillaQi Ke
    • G06F7/04G06F17/30
    • G06F21/6254Y10S707/99939
    • Strategies are described for sanitizing a data set, having the effect of obscuring restricted data in the data set to maintain its secrecy. The strategies operate by providing a production data set to a sanitizer. The sanitizer applies a data directory table to identify the location of restricted data items in the data set and to identify the respective sanitization tools to be applied to the restricted data items. The sanitizer then applies the identified sanitization tools to the identified restricted data items to produce a sanitized data set. A test environment receives the sanitized data set and performs testing, data mining, or some other application on the basis of the sanitized data set. Performing sanitization on a sanitized version of the production data set is advantageous because it preserves the state of the production data set. The data directory table also provides a flexible mechanism for applying sanitization tools to the production data set.
    • 描述了用于消毒数据集的策略,具有使数据集中的受限数据模糊以保持其保密性的效果。 通过向消毒剂提供生产数据集来操作策略。 消毒器应用数据目录表以识别数据集中受限数据项的位置,并识别要应用于受限制数据项的相应消毒工具。 然后,消毒剂将所识别的消毒工具应用于所识别的限制数据项目以产生消毒的数据集。 测试环境接收消毒数据集,并根据消毒数据集执行测试,数据挖掘或其他一些应用程序。 在生产数据集的消毒版本上执行清洁是有利的,因为它保留了生产数据集的状态。 数据目录表还提供了将清理工具应用于生产数据集的灵活机制。