What are the re-identification risk scores of publicly available anonymised clinical trial datasets?

There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community. Some anonymised datasets are now publicly available for secondary research. However, we do not know if they pose a privacy risk to the involved patients. We have 3 equations that can be used to calculate the re-identification risk scores using El-Emam’s[1] three derived risk metrics (equations) under the prosecutor and the journalist scenarios for an entire anonymised dataset, using information in the anonymised dataset. Re-identification risk score is estimated probability of any given individual being re-identified from an anonymised/de-identified dataset. The re-identification risk score depends on the variables available in the dataset, the number of observations in the dataset and on the strategy used to attack the dataset (prosecutor or journalist scenario). These equations only generate numbers, and they do not aim to actually re-identify individuals in the datasets. We aim to collect a broad random sample of publicly available, anonymised clinical trial datasets to calculate their re-identification risk scores. Step 1: We will contact data holders and request access to their anonymised datasets following the data owners’ local procedures. Step 2: Re-identification risk scores will be calculated for each dataset, using the 3 equations. Step 3: We will investigate what characteristics of the datasets are associated with increased or decreased risk score, compare the risk scores and their usability, and discuss our findings. To the best of our knowledge, this will be the first study to use these risk of re-identification scores across a range of clinical trials datasets.
[1] El Emam, K., Guide to the de-identification of personal health information. 2013: CRC Press.