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首页> 外文会议>2018 Second International Conference on Electronics, Communication and Aerospace Technology >NonHomogenous Anonymization Approach Using Association Rule Mining for Preserving Privacy
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NonHomogenous Anonymization Approach Using Association Rule Mining for Preserving Privacy

机译：使用关联规则挖掘保护隐私的非均匀匿名化方法

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摘要

Statistics reveals that with the advancement of technology, data sharing has become crucial for the purpose of research and analysis. Many organizations share their data with third party venders to gain information regarding the relevant and meaningful patterns that are hidden among the vast amount of raw data. But as the data needs to be shared with the third party, maintaining privacy of the individual becomes a challenging task. Thus, it becomes critical to share data in such a way so that privacy of the individual records is not hampered. This requirement has escalated the demand of research in a relatively new field that is privacy preserving data mining. Many techniques has been studied and developed related to this field which are based on either central server or distributed server. Some of these techniques include K-Anonymity, I-diversity, cryptographic method, randomization etc. Based on k-anonymity framework, nonhomogenous approach of generalization has already been proposed earlier. This approach of nonhomogenous anonymization provides privacy to the identity of individual records but suffers high privacy risk factor. The objective of the present work is to extend the existing approach of nonhomogenous generalization using association rule mining. Experimental results show that proposed technique performs better than existing technique based on two evaluation parameters - i.e. data disclosure Risk factor and execution time.
机译：统计数据表明，随着技术的进步，数据共享对于研究和分析变得至关重要。许多组织与第三方供应商共享其数据，以获取有关隐藏在大量原始数据中的相关且有意义的模式的信息。但是，由于需要与第三方共享数据，因此维护个人隐私成为一项艰巨的任务。因此，以不妨碍各个记录的隐私的方式共享数据变得至关重要。这项要求已经提升了一个相对较新的领域的研究需求，即隐私保护数据挖掘。已经研究和开发了与该领域相关的许多技术，这些技术基于中央服务器或分布式服务器。其中一些技术包括K-匿名性，I-多样性，密码方法，随机化等。基于k-匿名性框架，较早地提出了非均匀的泛化方法。这种非均匀匿名化的方法为个人记录的身份提供了隐私，但遭受了很高的隐私风险因素。当前工作的目的是扩展使用关联规则挖掘的非均匀泛化的现有方法。实验结果表明，基于两个评估参数（即数据披露风险因素和执行时间），提出的技术比现有技术具有更好的性能。

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