A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.

ABSTRACT:

Objectives

Clinical trial data sharing is crucial for promoting transparency and collaborative efforts in medical research. Differential privacy (DP) is a formal statistical technique for anonymizing shared data that balances privacy of individual records and accuracy of replicated results through a "privacy budget" parameter, ε. DP is considered the state of the art in privacy-protected data publication and is underutilized in clinical trial data sharing. This study is focused on identifying ε values for the sharing of clinical trial data.

Materials and methods

We analyzed 2 clinical trial datasets with privacy budget ε ranging from 0.01 to 10. Smaller values of ε entail adding greater amounts of random noise, with better privacy as a result. Comparison of rates, odds ratio

SUBMITTER: Chen H 

PROVIDER: S-EPMC11031247 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature