The security of privacy is currently a field of critical importance in data mining. Organizations and corporations in this dynamic environment are constantly trying to somehow get the database of their competitors. A detailed review of such databases can be done to retrieve a variety of confidential and sensitive information, links, connections, inferences, and findings. It can implicitly or explicitly cause a major loss to the database owner. The owners of the database sell their data to third parties for money. If a database is not held until it is revealed to a third party, then the data owner can suffer disasters. We consider the issue of collective publication of data to anonymize horizontally separated data on multiple data providers. The proposed work discusses and contributes to this new challenge of data publishing. First, we introduce the notion of group-based classification, which guarantees that the anonymized data satisfies a given privacy constraint against any group of data providers. We present a collaborative data publishing model with privacy preservation for efficiently checking in a group of records. Privacy preservation can be used to identify abnormal behavior in data sharing. Many privacy-preserving data publishing techniques were developed but failed to consider the data set because of its complexity and domain specific nature. Various organizations release information about persons in public for resource sharing. But maintaining the confidentiality of individual information is a difficult task with various data releases from multiple organizations where coordinating before data publication. The proposed model is compared with traditional models and the results depicts that proposed model exhibits better performance. © 2022 Scrivener Publishing LLC.