Abstract

Individuals increasingly leave behind information in resources managed by disparate organizations.  There is an interest in making this information available for a wide array of endeavors (e.g., policy assessment, discovery-based research, and surveillance activities).  Given the distribution of data, it is critical to ensure that it is sufficiently integrated before conducting any statistical investigation to prevent duplication (and thus overcounting of events) and fragmentation (and thus undercounting of events).  This problem is resolved through record linkage procedures, techniques that have been refined for over half a century.  However, these methods often rely on explicit- or potentially identifying features, which often conflict with the expectations of privacy regulations.  More recently, privacy preserving record linkage (PPRL) methods have been proposed which rely on randomized transformations of data, as well as cryptographically secure processing methods. However, it is often unclear how the various steps of a record lifecycle, including standardization, parameter estimation, blocking, record pair comparison, and ccommunication between all of the various parties involved in the process can take place. In this talk, I will review recent developments in PPRL methods, discuss how they have been engineered into working software systems, and provide examples of how they have been applied in several distributed networks in the healthcare community to facilitate biomedical research and epidemiological investigations.