Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.

中文翻译：

---

使用模糊化和匿名化操作的基于聚类的大数据隐私保护

数据挖掘器使用大数据进行分析，其中可能包含敏感信息。在此过程中，它对研究人员提出了某些隐私挑战。现有的隐私保护方法使用不同的算法，导致在保护敏感数据的同时限制数据重建。本文提出了一种基于聚类的大数据隐私保护概率模型，以保护敏感信息......模型以获得最小扰动和最大隐私。在我们的模型中，敏感信息在从数据簇中识别敏感数据以对其进行修改或概括后得到保护。分析所得数据集以计算我们模型在隐藏数据、重建丢失数据方面的准确度水平。进行了大量实验以证明我们提出的模型的结果。除了使用标准的性能评估措施之外，基于聚类的大数据中个体数据的隐私保护以最小的扰动和成功的重建突出了我们模型的重要性。