Supervised Learning used for Clinical Decision Support System

Abstract

The process of privacy preserving data publishing addresses the problem of revealing the sensitive information when mining for useful information. In the existing approaches the anonymization techniques provides one of the strongest privacy guarantees. This paper addresses the problem of private data publishing, where different attributes for the same set of individuals are held by multi-parties. To achieve this, the highly secure provider anonymization protocol is proposed propose an algorithm to securely integrate person-specific sensitive data from multiple data providers, whereby the integrated data still retain the essential information for supporting data mining tasks. This protocol used as sub-protocol for the exponential mechanism in a distributed setting. Further, the proposed algorithm helps to releases the data in a secure way according to the definition of secure multi party computation. The proposed algorithm can effectively preserve information for a data mining task.