A correlativity constrained(s,l)-diversity anonymity method based on clustering
Bin Zhang · Journal of Central South University(Science and Technology) · 2015
In allusion to the problem of traditional data anonymity models constructing equivalence class with high correlative sensitive values,(s,l)-diversity was proposed which limited the correlativity of sensitive values in the equivalence classes. This diversity model was based on traditional l-diversity model, and it measured the correlativity of the sensitive attribute values to decrease the information loss by equivalence classes with high corrective sensitive values. At the same time, a(s,l)-diversity clustering algorithm named SLCA was proposed to achieve(s,l)-diversity, and the SLCA algorithm measured the distance between tuples by measuring the correlativity of attribute values, which greatly decreased the information loss during data generation. The results show that SLCA algorithm is more effective in terms of both information loss and execution time, and SLCA algorithm can effectively decrease the correlativity of the sensitive values in the equivalence classes to protect the privacy security of the data sets.