Personalized l-Diversity Algorithm for Multiple Sensitive Attributes Based on Minimum Selected Degree First

Privacy preserving in data publishing (PPDP) is always an important issue in the fields of data mining and information security. So far, most of the research on privacy preserving technology is limited to single sensitive attribute, but there are a lot of data information which includes multiple sensitive attributes in the real life. In the meanwhile, more and more researchers are paying great attention to the personalized service in the process of privacy preserving as the requirements of personality put forward continuously. To expand the privacy preserving technology for single sensitive attribute and to satisfy the requirement of personalized service, the personalized privacy preserving approaches for multiple sensitive attributes in the process of data publishing are studied. Based on the single sensitive attribute l-diversity principle, a personalized multiple sensitive attributes l-diversity model is defined by introducing a personalized customization scheme based on domain hierarchies partitions. In the meanwhile, a multiple sensitive personalized l-diversity algorithm based on the minimum selected degree first (MSFMPL-diversity) is presented. The experimental results show that the proposed method not only can satisfy the requirement of individual personalized privacy, but also can protect the data privacy effectively and reduce the information hidden rate, which ensures the usability of the publishing data.