Large database are used in communication and storing systems. Each database is owned by a particular autonomous entity. For example, medical data by hospitals, income data by tax agencies, financial data by banks, and census data by statistical agencies. Likewise, the development of new models such as cloud computing increases the amount of data distributed between multiple entities. These distributed data can be integrated to enable better data analysis for making better decisions and providing high quality services. For example, data can be integrated to improve medical research, customer service, or homeland security. However, data integration between autonomous entities should be conducted in such a way that no more information than necessary is revealed between the participating entities. At the same time, new knowledge that results from the integration process should not be misused by adversaries to reveal sensitive information that was not available before the data integration. In this paper, we propose an algorithm to securely integrate person-specific sensitive data from multi data providers, whereby the integrated data still retain the essential information for supporting data mining tasks. We present the multi-party data publishing algorithm for vertically partitioned data that generate an integrated data table satisfying differential privacy. The algorithm also satisfies the security definition in the secure multiparty computation. The proposed algorithm can effectively preserve essential information for classification analysis.