Achieving Location Privacy in iOS Platform Using Location Privacy Framework

Rising popularity of location-services mobile applications and geotagging digitalactivities resulted in astonishing amount of mobility data collected from user devices, raising privacy concerns regarding the way this data is extracted and handled. Despite numerous studies concluded that human location trace is highly unique and poses great re-identification risks, modern mobile operating systems fell short of implementing granular location access mechanism. Existing binary location access resulted into location-based-services being able to retrieve precise user’s coordinates regardless of how much details their functionality actually require and sell it to data brokers. This paper aims to provide practical solution how a mobile operating system (iOS) can adopt a system that enforces better location privacy for user devices with Location Privacy Framework(LPF) that works as a trusted middleware between mobile operating system and third-party apps. LPF provides granulated way of extracting location-related data from device, maximizing privacy by applying geomasking algorithm based on minimum level of accuracy the app needs and ensuring k-anonymity with dummy-generation mechanisms. Furthermore, LPF enforces control over all location data network communication to and from the app to make sure that no identifying data is being shared with data brokers