A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
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DOI：
10.1197/jamia.m3144
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发表时间：
2009-09-01
影响因子：
6.4
通讯作者：
Bottomley, Jim
中科院分区：
文献类型：
--
作者：
El Emam, Khaled;Dankar, Fida Kamal;Bottomley, Jim
背景：隐私法中明确的患者同意要求可能会对健康研究产生负面影响，导致选择偏差和招募减少。如果收集或披露的信息被去识别化，通常会免除获得同意的立法要求。 目标：作者开发并实证评估了一种新的全局最佳去识别化算法，该算法满足 k-匿名标准，并且适合健康数据集。 设计：作者在六个公共、医院和登记数据集上，根据经验将 OLA（最佳格子匿名化）与三种现有的 k-匿名算法 Datafly、Samarati 和 Incognito 进行比较。 k 值和抑制限。测量：使用三个信息损失度量进行比较：精度、可辨别性度量和非均匀熵。还评估了每种算法的性能速度。结果：Datafly 和 Samarati 算法的信息丢失率高于 OLA 和 Incognito； OLA 在寻找全局最优的去标识化解决方案方面始终比 Incognito 更快。结论：对于健康数据集的去标识化，OLA 在信息丢失和性能方面是对现有 k-匿名算法的改进。
Background: Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified.Objective: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets.Design: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits.Measurement: Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated.Results: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution.Conclusions: For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.