【摘要】 针对现实数据集动态增加和多次发布的隐私保护需求,本文在分析增量更新数据匿名若干概化方式基础上,提出了防止数据重发布过程中发生隐私泄漏的单调概化原则,并利用该原则,设计一个基于划分的增量数据重发布k-匿名算法k-APPRP.理论分析和实验结果表明,算法k-APPRP可安全且高效地实现连续增长数据集重发布的隐私保护,同时保证发布数据具有较高的数据质量.更多还原

【Abstract】 Most of the previous works on k-anonymization focused on one-time release of data.However,data is often released continuously to serve various information purposes in reality.The purpose of this study is to develop an effective solution for the re-publication of incremental datasets.By analyzing several possible generalizations in the anonymization for incremental updates,an important monotonic generalization principle is proposed to prevent privacy disclosure in re-publication.Based on the monotonic generalization principle,a partitioning based privacy preserving k-anonymous algorithm k-APPRP for re-publication is proposed.The theoretical analysis and experimental results indicate that k-APPRP can securely anonymize a continuously growing dataset in an efficient manner while assuring high data quality.更多还原