This record is for the dataset “Decentralized geoprivacy: leveraging social trust on the distributed web” at <a href= "https://doi.org/10.6084/m9.figshare.12816164.v1">https://doi.org/10.6084/m9.figshare.12816164.v1</a>. <p><p> Despite several high-profile data breaches and business models that hinge on the commercialization of user data, participation in social media networks continues to require users to trust corporations to safeguard their personal data. Since these data increasingly contain geographic references that allow individuals’ locations and movements to be inferred, the need for new approaches to geoprivacy and data sovereignty has grown. We develop a geoprivacy framework for online social media networks that couples two emerging technologies, decentralized data storage and discrete global grid systems, to facilitate fine-grained user control over data ownership, access, and map-based representation. The framework is illustrated with a dynamic k-anonymity model that links geographic precision in information sharing to social trust as embedded in social network exchanges among users. In this framework, users’ spatio-temporal data are shared through a decentralized file system and are represented on a discrete global grid data model at spatial resolutions that correspond to varying degrees of trust between individuals who are exchanging information. Our geoprivacy framework has several advantages over centralized approaches to geoprivacy, namely trust in a third-party entity is not required and geoprivacy is dynamic and context-dependent with users maintaining autonomy. As distributed web applications begin to emerge, there is significant potential for developing the next generation of geographic information sharing tools with these technologies.<p><p>This data can be downloaded at <a href= "https://doi.org/10.6084/m9.figshare.12816164.v1">https://doi.org/10.6084/m9.figshare.12816164.v1</a>.