Decentralized geoprivacy: leveraging social trust on the distributed web

This record is for the dataset “Decentralized geoprivacy: leveraging social trust on the distributed web” at https://doi.org/10.6084/m9.figshare.12816164.v1. 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. This data can be downloaded at https://doi.org/10.6084/m9.figshare.12816164.v1.本数据集对应DOI为10.6084/m9.figshare.12816164.v1的《去中心化地理隐私：在分布式网络上利用社会信任》（Decentralized geoprivacy: leveraging social trust on the distributed web）。尽管频发备受瞩目的数据泄露事件，且诸多商业模式均以用户数据商业化作为核心依托，但社交媒体平台的用户仍需信任企业以保障其个人数据安全。随着此类数据日益包含可推断用户位置与移动轨迹的地理关联信息，针对地理隐私与数据主权的新型解决方案需求日益迫切。本研究提出一种面向社交媒体网络的地理隐私框架，该框架结合了两项新兴技术：去中心化数据存储（decentralized data storage）与离散全局网格系统（discrete global grid systems），以实现用户对数据所有权、访问权限及基于地图的展示形式的精细化管控。该框架通过动态k-匿名（k-anonymity）模型得以具象化，该模型将信息共享中的地理精度与用户社交网络互动中嵌入的社会信任度相关联。在此框架下，用户的时空数据（spatio-temporal data）通过去中心化文件系统（decentralized file system）进行共享，并基于离散全局网格数据模型进行展示，其空间分辨率与信息交换双方之间的信任程度相对应。相较于中心化地理隐私解决方案，本地理隐私框架具备多项优势：无需依赖第三方实体的信任，且地理隐私具备动态性与场景依赖性，用户可保持数据自主权。随着分布式网络应用的兴起，依托此类技术开发下一代地理信息共享工具具备巨大潜力。本数据集可通过链接https://doi.org/10.6084/m9.figshare.12816164.v1下载。