Achieving(k,l)-Diversity in Privacy Preserving Data Publishing
Guangli Zhu · 2013
In order to avoid disclosure of individual identity and sensitive attribute,reduce the information loss when data release,a clustering-based algorithm to achieve(k,l)-diversity(CBAD)in data publishing was presented.The discrete attributes and continuous attributes mixed in the data set were fully taken into account while clustering.The probability distribution was used as metrics to measure similarity between the data objects.We solved the confusion of the information loss and the distance between data objects,pointed out that the clustering-based optimization(k,l)-diversity algorithm is NP-hard problem,proposed the concept of privacy protection degree with parameter k and l,and analysed the complexity of the algorithm.Theoretical analysis and experimental results show that the method can effectively reduce the execution time and information loss,improve query precision.