An Enhanced Approach for Privacy Preserving Record Linkage during Data Integration

N. Shekokar, V. Shelake
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引用次数: 4

Abstract

Today collecting and integrating data from multiple datasets has become a vital part to perform various analysis tasks. Record linkage plays an important component during data integration to detect and link similar data instances. However, personal and sensitive data need to be protected in a manner so that there is no re-identification of original attribute values by the party performing record linkage. Now-a-days, the Bloom filter encoding has utilized across many countries for privacy preserving record linkage. Moreover, the hardened approaches of Bloom filter encoding enhance privacy at the cost of reduced linkage accuracy. Still the security concerns remain with the Bloom filter encoding techniques because attackers can re-identify the obfuscated data with the use of available public resources. We propose an enhanced approach for privacy preserving record linkage (EPPRL) during data integration to achieve better privacy with acceptable linkage accuracy. The results show that the proposed approach EPPRL outperforms in comparison with Balanced Bloom filter encoding technique in terms of precision, recall, f-measure and re-identification of attribute values.
一种增强的数据集成过程中保护隐私的记录链接方法
如今,从多个数据集收集和集成数据已成为执行各种分析任务的重要组成部分。记录链接是数据集成过程中检测和链接相似数据实例的重要组成部分。但是,个人和敏感数据需要以某种方式加以保护,以便执行记录链接的一方不会重新识别原始属性值。如今,布鲁姆过滤器编码已在许多国家用于隐私保护记录链接。此外,强化的布隆过滤器编码方法以降低链接精度为代价增强了隐私性。但是,Bloom过滤器编码技术仍然存在安全问题,因为攻击者可以使用可用的公共资源重新识别被混淆的数据。我们提出了一种增强的数据集成过程中隐私保护记录链接(EPPRL)的方法,以获得更好的隐私和可接受的链接精度。结果表明,该方法在属性值的查全率、查全率、f-测度和重识别等方面均优于平衡布隆滤波编码技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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