This article proposes the individual data privacy during collaborative computation in data mining method using an optimization model. The privacy problem is solved using different methodologies. The solution for individual privacy is considered as a multi-objective optimization model. Practically, the requirement for privacy varies from user to user. Therefore, it generates inherent vagueness for individual privacy. In this article, the vagueness is considered and the privacy problem is solved by a fuzzy optimization method. The fuzzy multi-objective optimization model is proposed to be used as a supplementary privacy method to address individual privacy issues. The fuzzy constraints are generated to solve the models on the basis of the privacy requirements of users. The fuzzy set domain for the optimization problem is used to fulfil the individual privacy requirements in a computing environment. The proposed solution allows data owners to choose their own privacy level on demand, with maximum flexibility.

本文提出了使用优化模型的数据挖掘方法中协同计算过程中的个体数据隐私。使用不同的方法解决隐私问题。个人隐私的解决方案被认为是一个多目标优化模型。实际上，对隐私的要求因用户而异。因此，它对个人隐私产生了固有的模糊性。本文考虑模糊性，采用模糊优化方法解决隐私问题。提出了模糊多目标优化模型作为补充隐私方法来解决个人隐私问题。根据用户的隐私要求生成模糊约束来求解模型。优化问题的模糊集域用于满足计算环境中的个人隐私要求。提议的解决方案允许数据所有者根据需要选择自己的隐私级别，具有最大的灵活性。