A new model for privacy preserving multiparty collaborative data mining

Due to the increasing use of internet, the privacy of sensitive data in multiparty collaborative mining is a major issue. The group of participants contribute their own datasets and collaboratively involved to find quality model in multiparty collaborative mining. In this approach, each participant has sensitive and non-sensitive data in their local database. Therefore, an important challenge of privacy preserving collaborative data mining (PPCDM) is how multiple parties efficiently conduct data mining without exposing each participant´s sensitive information. This paper proposes a new Binary Integer Programming model for multiparty collaborative data mining, which provide solutions to investigated problem of disclosure of sensitive data. In addition to that, maintaining confidentiality of the newly created pooled data by semantically secured ElGamal Encryption Scheme. Finally, Artificial Neural Network is used by the service provider in order to predict the patterns for data providers to identify the risk factors of colorectal cancer.