Data Preserving for Data Publishing: For Two Sensitive Attributes
The sensitive attribute such as social security number, disease by some organization should contain privacy in data publishing. Data holders can remove some attributes to gain privacy but other attributes which are in published data can lead to reveal privacy to adversary. So several methods such as K-anonymity, L-diversity, T-closeness are come into existence to maintain privacy in data publishing. The authors propose (n, t) closeness with two sensitive attribute. Suppose, they have two sensitive attributes like salary and disease. They can consider two attributes separately i.e. an equivalence class has (n, t) closeness if E has (n, t) closeness with respect to both sensitive attribute salary and disease.