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A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi-records datasets
Transactions on Emerging Telecommunications Technologies ( IF 3.6 ) Pub Date : 2020-11-30 , DOI: 10.1002/ett.4180
Tehsin Kanwal 1 , Adeel Anjum 1, 2 , Abid Khan 3 , Alia Asheralieva 2 , Gwanggil Jeon 4
Affiliation  

The collection of private health data without compromising privacy is an imperative aspect of privacy-aware data collection mechanisms. Privacy-preserved data collection is achieved by anonymizing private data before its transmission from data holders to data collectors. Though there exist ample literature on private data collection for 1:1 (single record of a data holder) datasets, collecting multi-records (multiple records of a data holder) datasets (referred to as 1:M datasets) has not received due attention from the research community. Therefore, the current studies experience serious privacy breaches in 1:M dataset thereby limiting their application in secure healthcare applications and systems. In this work, we have formally classified main privacy disclosures on these data collection mechanisms and proposed an improved privacy scheme, namely, horizontal sliced permuted permutation (H-SPP) for 1:M datasets. It uses the composite slicing and anatomy-based approach to protect against the privacy violations like identity, attribute, and membership disclosures. Moreover, we perform formal modeling of the proposed scheme using high-level Petri nets (HLPN) and show that it effectively prevents the identified external and internal privacy attacks. Experimental results show that H-SPP provides robust privacy for health data with high performance.

中文翻译:

一个正式的对抗视角:多记录数据集的安全高效的电子健康记录收集方案

在不损害隐私的情况下收集私人健康数据是隐私意识数据收集机制的一个必要方面。隐私保护的数据收集是通过在将私人数据从数据持有者传输到数据收集者之前对其进行匿名化来实现的。尽管有大量关于 1:1(数据持有者的单个记录)数据集的私有数据收集的文献,但收集多记录(数据持有者的多个记录)数据集(称为 1:M 数据集)尚未得到应有的关注来自研究界。因此,当前的研究在 1:M 数据集中经历了严重的隐私泄露,从而限制了它们在安全医疗保健应用程序和系统中的应用。在这项工作中,我们对这些数据收集机制的主要隐私披露进行了正式分类,并提出了改进的隐私方案,1:M 数据集的水平切片置换置换 (H-SPP)。它使用复合切片和基于解剖结构的方法来防止身份、属性和成员身份披露等隐私侵犯。此外,我们使用高级 Petri 网(HLPN)对所提出的方案进行正式建模,并表明它有效地防止了已识别的外部和内部隐私攻击。实验结果表明,H-SPP 为健康数据提供了高性能的鲁棒隐私。
更新日期:2020-11-30
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