隐私保护数据挖掘方法的分类与评价

Negar Nasiri, M. Keyvanpour
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引用次数: 1

摘要

在最近的时代,信息量呈指数级增长。这些数据可用于商业、医疗保健、网络安全等多个领域。从原始信息中提取有用的知识是一个重要的过程。但这个过程中的挑战是这些信息的敏感性,这使得业主不愿意分享敏感信息。这使得数据挖掘中数据隐私的研究成为当今的一个热门话题。在我们的论文中,目的是准备一个定性分析方法的框架。这一定性框架包括三个主要部分:拟议方法的全面分类、拟议评价标准及其定性评价。我们提出这个框架的主要目的是1)系统地介绍数据挖掘中最重要的隐私保护方法2)为这些方法的定性比较创建一个合适的平台3)提供选择适合应用领域需求的方法的可能性4)系统地介绍现有方法的缺点,作为改进PPDM方法的先决条件。
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Classification and Evaluation of Privacy Preserving Data Mining Methods
In the recently age, the volume of information is growing exponentially. This data can be used in several fields such as business, healthcare, cyber security, etc. Extracting useful knowledge from raw information is an important process. But the challenge in this process is the sensitivity of this information, which has made owners unwilling to share sensitive information. This has led the study of the privacy of data in data mining to be a hot topic today. In our paper, an aim is made to prepare a framework for qualitative analysis of methods. This qualitative framework consists of three main sections: a comprehensive classification of proposed methods, proposed evaluation criteria and their qualitative evaluation. Our main purpose of presenting this framework is 1) systematic introduction of the most important methods of privacy preserving in data mining 2) creating a suitable platform for qualitative comparison of these methods 3) providing the possibility of selecting methods appropriate to the needs of application areas 4) systematic introduction of points Weakness of existing methods as a prerequisite for improving methods of PPDM.
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