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An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification

机译:一个保护个人隐私的智能框架,对数据挖掘分类进行实证评估

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Along with rapid technological advancements, the need for developing suitable frameworks for protecting privacy of individuals becomes essential for the wide-spread acceptance of knowledge-based applications. Privacy Preserving Data Mining has become an active area of research recently to address privacy issues whenever the data is to be provided for a variety of purposes like survey, research etc. Several remarkable frameworks are being developed, but there is not enough sensible solution for considering both privacy and information evenly. Privacy mechanisms which compromise with the information usually weaken the quality of data mining results. An intelligent framework to address this issue is proposed in this paper which also discusses empirical results on classification using original health care data related to Indian population, namely NFHS-3 and shows the effectiveness of our approach.
机译:随着技术的飞速发展,对于保护基于知识的应用程序的广泛接受,需要开发合适的框架来保护个人隐私变得至关重要。隐私保护数据挖掘已成为最近研究的一个活跃领域,无论何时出于各种目的(例如调查,研究等)提供数据时,隐私保护问题都将得到解决。正在开发一些出色的框架,但是尚无足够明智的解决方案可供考虑隐私和信息均等。与信息妥协的隐私机制通常会削弱数据挖掘结果的质量。本文提出了一个解决此问题的智能框架,该框架还讨论了使用与印度人口相关的原始医疗保健数据NFHS-3进行分类的经验结果,并显示了我们方法的有效性。

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