Applying Triple-Matrix Masking for Privacy Preserving Data Collection and Sharing in HIV Studies

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.