Privacy-preserving data publishing means anonymizing the data for research and analysis purposes. For the data publication, a huge amount of research work has a single sensitive attribute. However, the researchers had little concern about the practical scenarios by PPDP with Multiple Sensitive Attributes (MSAs). The protection of the revelation of personal information represents one of the vital parts of PPDP. A number of privacy-preserving techniques for data publication have been introduced. But still, there are several drawbacks, such as it can't be used with a dataset of size 1; there is no trade-off between utility and classification privacy, and M. This work, therefore, proposes the P+K-QIAB and F-ZeroR to preserve privacy for multiple records and the resulting privacy versus classification utility trade-off. To satisfy the multi-record privacy, we propose a balanced P+Sensitive K anonymity model along with the P+K Quasi Identifier Bucket (P+K-Qiab) algorithm. This is an extension approach of the p+-sensitive k-anonymity method. Further, to satisfy the trade-off between classification privacy and utility, a hybrid Fuzzy with ZeroR (F–ZeroR) classification is used. The results of our experiments showed that PPDP improves privacy and classification utility.