分组随机化隐私保护频繁模式挖掘

已有的隐私保护频繁模式挖掘随机化方法不考虑隐私保护需求差异性，对所有个体运用统一的随机化参数，实施同等的保护，无法满足个体对隐私的偏好.提出基于分组随机化的隐私保护频繁模式挖掘方法（grouping-based randomization for privacy preserving frequent pattern mining，简称GR-PPFM）.该方法根据不同个体的隐私保护要求进行分组，为每一组数据设置不同的隐私保护级别和与之相适应的随机化参数.在合成数据和真实数据中的实验结果表明：相对于统一单参数随机化mask，分组多参数随机化GR-PPFM不仅能够满足不同群体多样化的隐私保护需求，还能在整体隐私保护度相同情况下提高挖掘结果的准确性.

Privacy Preserving Frequent Pattern Mining Based on Grouping Randomization

Existing randomization methods of privacy preserving frequent pattern mining use a uniform randomization parameter for all individuals, without considering the differences of privacy requirements. This equal protection cannot satisfy individual preferences for privacy. This study proposes a method of privacy preserving frequent pattern mining based on grouping randomization (referred to as GR-PPFM). In this method, individuals are grouped according to their different privacy protection requirements. Different group of data is assigned to different privacy protection level and corresponding random parameter. The experimental results of both synthetic and real- world data show that compared with the uniform single parameter randomization of mask, grouping randomization with multi parameters of GR-PPFM can not only meet the needs of different groups of diverse privacy protection, but also improve the accuracy of mining results with the same overall privacy protection.