Research On Privacy-Preserve Data Mining For Protecting Association Rule And Original Datasets

Data mining has long been an active area of database research. In the field of science research or business application, data mining both has gained pleasing achievement. However, accompanying such benefits are concerns about information privacy. Because of these concerns, some people might decide to give false information in fear of privacy problem, or they might simply refuse to divulge any information at all.So privacy is an important issue in data mining and knowledge discovery. Design and analysis of privacy preserving data mining is meaningful and has attracted much interest in this field.In this paper, we proposal new methods to protect privacy based on traditional technique of association rules data mining. According to different protected objects, this paper is divide into two parts generally. One part is about how to hide association rules, and another is about how to hide original dataset to mining rules.Based on the framework provided by Stanley R.M.Oliveria etc, against the shortcoming of grouping complexity of the IGA algorithm, this paper proposals IF-IGA algorithm based on inverted files. Against the shortcoming of deleting especial item of every sensitive rule in MinFIA and MaxFIA algorithms etc, this paper proposals VBA algorithm to delete items in sensitive rules in balance, striving to gain a balance in association rule privacy preserving and reducing effect to the original dataset.Against the disadvantage of in exist algorithms of privacy preserving association rule, this paper delivers a new association rule mining algorithm for protecting privacy of original datasets. It can in effect preserve original datasets and guarantee accuracy. And it can use algorithm of association rule mining in exist, making the practical operation effective.And we make use of Synthetic Data Generator to provide corresponding experiment to prove the precision and effectiveness of our algorithms.