TY -的盟Templ马提亚盟——Kanjala Chifundo盟——siem Inken PY - 2022 DA - 2022/9/2 TI -隐私的研究参与者开放获取的健康和人口监测系统数据:需求分析的数据匿名化乔- JMIR公共卫生Surveill SP - e34472六世- 8 - 9千瓦-纵向数据和事件历史数据KW -低收入和中等收入国家KW - LMIC KW -匿名化KW -健康和人口监测系统AB -背景:数据匿名化和数据共享已成为全球个人、组织和国家的热门话题。只要可以保留数据的效用，并且可以将泄露的风险控制在可接受的水平以下，对包含个人敏感信息的匿名数据进行开放访问共享是最有意义的。在这种情况下，研究人员可以不受访问限制地使用数据。目的:本研究旨在强调共享健康监测事件历史数据的要求和可能的解决方案。挑战在于对多个事件日期和时变变量进行匿名化。方法:提出了一种增加事件日期噪声的顺序方法。这种方法维护事件顺序并保留事件之间的平均时间。此外，提出了一种基于噪声邻居距离的匹配方法来估计风险。对于随时间变化的关键变量，如教育水平或职业，我们提出了两个建议:一个是基于限制个人的中间状态，另一个是在数据子集中实现k-匿名。 The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information. Results: An anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created. Conclusions: The proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker’s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2022/9/e34472 UR - https://doi.org/10.2196/34472 UR - http://www.ncbi.nlm.nih.gov/pubmed/36053573 DO - 10.2196/34472 ID - info:doi/10.2196/34472 ER -