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Efficient E-health Data Release with Consistency Guarantee under Differential Privacy

机译:高效的电子健康数据在差异隐私下具有一致性保证

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E-health data release, which answers the statistical queries of the Electronic Health Records (EHRs), has been widely adopted in modern health care services. However, since the EHRs contain sensitive information of the patients, the data release procedure may lead to the leakage of the privacy of patients if it is done without necessary protection measures in place. On addressing this, existing research literature introduces differential privacy to provide the necessary privacy guarantee. However, it is not suitable in sensitive e-health environments because it lacks the efficiency for data processing and updating. In this paper, we propose an efficient e-health data release scheme with consistency guarantee under differential privacy. Specifically, we improve the performance of the previous work by designing a new private partition algorithm of histogram and also proposing a heuristic hierarchical query method. We conduct real experiments and compare our scheme with the existing one to show that the proposal is more efficient in terms of data processing and updating. Moreover, we increase the accuracy of data release through consistency and give proof of privacy to show that the proposed algorithm is under ε-differential privacy.
机译:电子健康数据发布,回答电子健康记录(EHRS)的统计查询,已广泛采用现代保健服务。然而,由于EHRS含有患者的敏感信息,因此数据发布程序可能导致患者隐私泄漏,如果没有必要的保护措施。在解决这一点上,现有的研究文献介绍了差异隐私,以提供必要的隐私保障。但是,它不适合于敏感的电子健康环境,因为它缺乏数据处理和更新的效率。在本文中,我们提出了一种高效的电子健康数据释放方案,并在差异隐私下的一致性保证。具体而言,我们通过设计直方图的新的私有分区算法来提高先前工作的性能,并提出启发式分层查询方法。我们进行真实的实验,并将我们的计划与现有的计划进行比较,以表明该提案在数据处理和更新方面更有效。此外,我们通过一致性提高数据释放的准确性,并赋予隐私证明,以表明所提出的算法属于ε差异隐私。

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