Pei Qinglin, Chen Shigang, Xiao Yao, Wu Samuel S
Department of Biostatistics, University of Florida, Gainesville, USA.
Curr HIV Res. 2016;14(2):121-9. doi: 10.2174/1570162x13666151029102539.
Many HIV research projects are plagued by the high missing rate of selfreported information during data collection. Also, due to the sensitive nature of the HIV research data, privacy protection is always a concern for data sharing in HIV studies.
This paper applies a data masking approach, called triple-matrix masking [1], to the context of HIV research for ensuring privacy protection during the process of data collection and data sharing.
Using a set of generated HIV patient data, we show step by step how the data are randomly transformed (masked) before leaving the patients' individual data collection device (which ensures that nobody sees the actual data) and how the masked data are further transformed by a masking service provider and a data collector. We demonstrate that the masked data retain statistical utility of the original data, yielding the exactly same inference results in the planned logistic regression on the effect of age on the adherence to antiretroviral therapy and in the Cox proportional hazard model for the age effect on time to viral load suppression.
Privacy-preserving data collection method may help resolve the privacy protection issue in HIV research. The individual sensitive data can be completely hidden while the same inference results can still be obtained from the masked data, with the use of common statistical analysis methods.
许多艾滋病病毒（HIV）研究项目在数据收集过程中都受到自我报告信息高缺失率的困扰。此外，由于HIV研究数据的敏感性，隐私保护一直是HIV研究数据共享中的一个关注点。
本文将一种名为三重矩阵掩码的数据掩码方法应用于HIV研究背景下，以确保在数据收集和数据共享过程中的隐私保护。
使用一组生成的HIV患者数据，我们逐步展示了数据在离开患者个人数据收集设备之前是如何被随机转换（掩码处理）的（这确保了没有人能看到实际数据），以及掩码数据是如何由掩码服务提供商和数据收集者进一步转换的。我们证明，掩码数据保留了原始数据的统计效用，在关于年龄对抗逆转录病毒疗法依从性影响的计划逻辑回归以及关于年龄对病毒载量抑制时间影响的Cox比例风险模型中，得出了完全相同的推断结果。
隐私保护数据收集方法可能有助于解决HIV研究中的隐私保护问题。通过使用常见的统计分析方法，在完全隐藏个体敏感数据的同时，仍可从掩码数据中获得相同的推断结果。
