Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. existing models multiple sensitive attributes do not concentrate on the correlation among attributes, which in turn leads much utility loss. An efficient model Heap Bucketization-anonymity (HBA) proposed balance with attributes. used anatomization vertically partition into 1. Quasi-identifier table 2. Sensitive attribute table. quasi-identifier anonymized by implementing k-anonymity slicing are applying Bucketization. metrics Normalized Certainty Penalty KL-divergence have compute loss patient dataset. experimental results show that HB-anonymity can significantly achieve high less than other models. only balances also eradicates i) background knowledge attack, ii) attack iii) membership iv) non-membership v) fingerprint attack.