The Application of Random Orthogonal Transformations in Privacy Preserving Association Rules Mining
Chu Lili · Science Technology and Industry · 2010
Privacy preserving is an important direction for data mining research.This paper is concentrated on the issue of protesting the underlying attribute values when sharing data for association rules mining,adopts a random orthogonal transformation method without depending on any concrete data and thereby solve the computational problems when handling large data sets.And then evaluates the privacy preserving degree of the random orthogonal transformation using the combination of the traditional evaluation method for privacy preserving degree and the direction privacy preserving degree,thus makes the results be more in line with the actual situation.Theoretical analysis and demonstrations shows that the method in this paper has a very good privacy,efficient and applicability.