The privacy protection study against incremental updates
Xiaolin Zhang, Li su-wei · 2010
Static privacy protection technologies available are not well protected already published data, and dynamic protection technology is becoming a research hotspot. In this paper we propose an effective method of privacy protection based on dynamic protection technology, analyzing how inferences from multiple releases may temper the category of privacy and resolving the problems of privace loss of inference tables be made of multiple releases tables. Using space-filling curve to multi-dimensional quasi-identifiers into a one-dimensional quasi-identifiers, solving the situation of a high degree of information loss. Experiments not only show that the running time of this method is linear time but also show that the method can guarantee k-anonymity and l-diversity of many published tables.