Privacy-preserving data integration using decoupled data

Abstract

Data from social network websites are an excellent source of information for studying human behavior and interactions. Typically, when analyzing such data, the default mode of access is de-identified data, which provides a level of privacy protection. However, due to its inability to link to other data, de-identified data has limitations with regard to answering broad and critically important questions about our complex society. In this study, we investigate the properties of information related to privacy, and we present a novel model of data access called decoupled data access for studying personal data using these properties. “Decoupling” refers to separating out the identifying information from the sensitive data that needs protection. We suggest that decoupled data access can provide flexible data integration with error management while providing the same level of privacy protection as deidentified data. We further test the ability of different mechanisms to hinder inference of identity when names are revealed for data integration. Our results show that through chaffing, not specifying the universe around the data, and revealing names in isolation, the real identities of names for both common and rare names can be protected.