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K-Anonymization-Based Temporal Attack Risk Detection Using Machine Learning Paradigms
Journal of Circuits, Systems and Computers ( IF 1.5 ) Pub Date : 2020-09-10 , DOI: 10.1142/s021812662150050x
Geetha Peethambaran 1 , Chandrakant Naikodi 2 , Suresh Lakshmi Narasimha Setty 3
Affiliation  

Huge amount of personal data is collected by online applications and its protection based on privacy has brought a lot of major challenging issues. Hence, the K-anonymization with privacy-preserving data publishing has emerged as an active research field. The published data contains personalized information, which may be used for analysis converting it to useful information. In this paper, Quasi identifier (QI) data publishing with data preservation through the K-anonymization process is proposed. Moreover, the risks such as the temporal attack in the previous release of re-identifying QI information are evaluated using the K-anonymity model. The development of independent and ensemble classifiers for finding efficient QI’s to avoid temporal attacks is the major objective of this paper. Therefore, the classifiers like Naïve Bayes, Support Vector Machine, and Multilayer Perceptron are used as base classifiers. An ensemble model based on these base classifiers is also used. The experimental results demonstrate that, the proposed classification approach is an effective K-anonymity tool for the enhancement of sequential release.

中文翻译:

使用机器学习范式进行基于 K 匿名化的时间攻击风险检测

在线应用程序收集了大量的个人数据,其基于隐私的保护带来了许多重大的挑战问题。因此,ķ- 隐私保护数据发布的匿名化已成为一个活跃的研究领域。发布的数据包含个性化信息,可用于将其转换为有用信息的分析。在本文中,准标识符(QI)数据发布与数据保存通过ķ- 提出了匿名化过程。此外,之前发布的重识别 QI 信息中的时间攻击等风险是使用ķ-匿名模型。开发独立和集成分类器以找到有效的 QI 以避免时间攻击是本文的主要目标。因此,使用朴素贝叶斯、支持向量机和多层感知器等分类器作为基分类器。还使用了基于这些基本分类器的集成模型。实验结果表明,所提出的分类方法是一种有效的 K-匿名工具,可用于增强顺序发布。
更新日期:2020-09-10
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