Data privacy protection based on sensitive attributes dynamic update
Jun Bi Liao, Chaohui Jiang, Chun Guo · 2016
Focused on the issue that the existing static data anonymous algorithm give dynamic data publication directly bring unexpected disclosure of private information frequently, based on the m-invariance model, an sensitive attribute of dynamic update f or privacy preserving algorithm (SDUPPA)is propose d. The algorithm breaks the table down quasi-identifier and sensitive attribute in data re-publication, which effectively protects data privacy. At the same time, it adopts orderly random switching technology to disturb sensitive attributes, so that the attacker ca n not accurately distinguish individual sensitive attribute information. Because SDUPPA algorithm meets the m-invariance privacy protection requirement and takes local generalization for quasi-identifier attributes, which reduces the information loss of over anonymous processing, so to some extent ensured the data quality. Research and analysis through data quality show that the SDUPPA algorithm can efficiently reduce the information loss and improve data security of the algorithm in dynamic data update environment.