\ud83d\udccd Mapping the Privacy Risks Hidden in Geolocation Ecosystems<\/p>\n\n\n\n
Our paper titled\u00a0“SoK: Mapping the Privacy Landscape of Geolocation Ecosystems”<\/em>\u00a0has been accepted and appeared at PETS 2026.<\/p>\n\n\n\n Our paper takes a systematic look at how geolocation ecosystems produce location data \u2014 and what that means for user privacy.<\/p>\n\n\n\n THE PROBLEM:<\/strong> Geolocation is everywhere \u2014 from OS-level location services to object-tracking platforms \u2014 but the architectures behind it are diverse, often proprietary, and studied only in isolation. The result is a fragmented understanding of privacy risks: we know individual systems leak location data, but we lack a global picture of why<\/em>, and whether different ecosystems share common structural vulnerabilities.<\/p>\n\n\n\n WHAT WE DO:<\/strong> We introduce an abstract model of geolocation ecosystems and a systematic methodology for analyzing their privacy implications. Applying it across nine representative case studies \u2014 spanning OS-level services, IP geolocation, Wi-Fi positioning, and object-tracking platforms \u2014 we show that many privacy risks are not bugs or poor design, but direct consequences of architectural choices<\/em> common across heterogeneous systems. Our comparative analysis identifies structural patterns that significantly impact user privacy and exposes gaps in existing defenses, pointing toward new research directions for building privacy into future geolocation architectures from the ground up.<\/p>","protected":false},"excerpt":{"rendered":" \ud83d\udccd Mapping the Privacy Risks Hidden in Geolocation Ecosystems Our paper titled\u00a0“SoK: Mapping the Privacy Landscape of Geolocation Ecosystems”\u00a0has been accepted and appeared at PETS 2026. Our paper takes a systematic look at how geolocation ecosystems produce location data \u2014 and what that means for user privacy. THE PROBLEM: Geolocation is\u2026<\/p>\n