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.