数据挖掘中的隐私泄漏问题一直备受关注，在确保隐私的前提下达到最佳挖掘效果是近年来数据挖掘领域的研究热点之一。为防止在数据挖掘中发生隐私泄漏等问题，基于隐私保护框架，提出一种支持动态计算冲突度的高效的敏感规则清洗算法。在隐藏敏感规则的同时，动态调整冲突交易的冲突度，以尽量减少对非敏感规则误隐藏的可能性。理论分析与实验结果表明，给出的算法隐藏失败率为零，且大幅度降低了误隐藏率，有效保护了敏感规则，显著改善了算法的清洗效果。%The privacy leaking issue of data mining is always drawing tremendous attention. Realizing optimal mining effect without priva-cy leaking is one of the active issues in the field of data mining. In order to prevent privacy leaking during data mining,based on privacy enforcing framework,propose a sensitive rule sanitization algorithm that supports dynamic degree of conflict calculation,which reduces misses costs to the best via altering conflict transaction record dynamically while achieving good concealing purpose. Theoretical analysis and experiment results show that the presented algorithm can protect the sensitive rules effectively with no hiding failure and reduce re-markably the error hiding rate,which enhances the performance of the algorithm significantly.
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