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A new model for privacy preserving multiparty collaborative data mining

机译：隐私保护多方协作数据挖掘的新模型

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摘要

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.
机译：由于互联网的使用不断增加，多方协作挖掘中敏感数据的隐私成为一个主要问题。参与者小组贡献自己的数据集，并共同参与以寻找多方协作挖掘中的质量模型。通过这种方法，每个参与者在其本地数据库中都有敏感和非敏感数据。因此，隐私保护协作数据挖掘（PPCDM）的一个重要挑战是多方如何有效地进行数据挖掘而不暴露每个参与者的敏感信息。本文提出了一种新的用于多方协作数据挖掘的二进制整数编程模型，为研究敏感数据的公开问题提供了解决方案。除此之外，通过语义安全的ElGamal加密方案维护新创建的合并数据的机密性。最后，服务提供商使用人工神经网络来预测数据提供商识别大肠癌危险因素的模式。

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