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Re-identification of medical records by optimum quasi-identifiers

机译：通过最佳准标识符对病历进行重新识别

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摘要

Recently, medical records are shared to online for a purpose of medical research and expert opinion. There is a problem with sharing the medical records. If someone knows the subject of the record by using various methods, it can result in an invasion of the patient's privacy. De-identification techniques are applicable to address the problem, however, de-identified data has a risk of re-identification. For this reason, if de-identification techniques are not sufficient, it may increase a risk of re-identification. On the contrary, if the techniques are too excessive, data utility may be damaged. Meanwhile, de-identified data can be re-identified from inference using background knowledge. The objective of this paper is to analyze the probability of re-identification according to inferable quasi-identifiers. We analyzed factors, inferable quasi-identifiers, which can be inferred from background knowledge. Then, we estimated the probability of re-identification from taking advantage of the factors. As a result, we determined the effect of the re-identification according to the type and the range of inferable quasi-identifiers. This paper contributes to a decision on de-identification target and level for protecting patient's privacy through a comparative analysis of the probability of re-identification according to the type and the range of inference.
机译：最近，出于医学研究和专家意见的目的，医疗记录被在线共享。共享病历存在问题。如果有人通过各种方法知道记录的主题，则可能导致患者隐私受到侵犯。取消标识技术适用于解决该问题，但是，取消标识的数据有重新标识的风险。因此，如果取消识别技术不足，则可能会增加重新识别的风险。相反，如果技术过高，则可能会损坏数据实用程序。同时，可以使用背景知识从推理中重新识别未识别的数据。本文的目的是根据可推断的准标识符分析重新识别的概率。我们分析了可以推断的准标识符的因素，这些因素可以从背景知识中推断出来。然后，我们利用这些因素估计了重新识别的可能性。结果，我们根据可推断的准标识符的类型和范围确定了重新标识的效果。通过根据推断的类型和范围对重新识别的可能性进行比较分析，本文有助于确定用于保护患者隐私的非识别目标和级别。

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