Formal Anonymity Models for Efficient Privacy-Preserving Joins
Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, the authors present a secure multiparty computation protocol that enables record joins via consumers' encrypted identifiers.