Research On Privacy Preserving Association Rules Mining

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 thesis, the author studies privacy preserving association rules mining. First introduces the relevant background knowledge and analyzes and introduces the existing typical privacy preserving association rules mining, and then analyzes the characteristics and limitations of a typical algorithm called Apriori. As well as describing privacy protection association rule mining algorithm called MASK algorithm in detail, and makes a comparison between MASK algorithm and Apriori algorithm in running time; MASK algorithm for the existence of problems and their causes are analyzed in detail.On this basis, Object from the privacy point of view of the original data set for mining association rules on how to protect data privacy issues, first from the perspective of improving the data storage structure, the use of mathematical set theory, changes the data storage means, thereby reducing the reconstruction of the original data support the process of scanning the number of databases, eliminating data entry support reconstruction of the original complexity of index, and its description is given. Then, in the transformed dataset, first performs cluster analysis to obtain normalized data, and then mines association rules, and evaluates the privacy preserving degree of the matrix transformation using the combination of the traditional evaluation method and the direction privacy preserving degree. The algorithm has solved some problems effectively, such as the problem that some special values are inconsistent with the actual values according to the traditional evaluation method and the computational problems when handling large data sets. Theoretical analysis and demonstrations show that the method in this paper has very good privacy, efficiency and applicability.The thesis concludes with an application of the algorithm in this paper in knowledge sharing of collaborative commerce. It analyzes the application background of this algorithm, and then presents elaborate detailed application process of the algorithm, puts up a preliminary evaluation of the results.