부산대학교 도서관

Population density and natural and man-made disasters make public safety a concern of growing importance. In this paper we aim to enable the vision of smart and safe cities by exploiting mobile and social networking technologies to securely and privately extract, model and embed real-time public safety information into quotidian user experiences. We first propose novel approaches to define location- and user-based safety metrics. We evaluate the ability of existing forecasting techniques to predict future safety values. We introduce iSafe, a privacy-preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. We present implementation details of iSafe as both an Android application and a browser plugin that visualizes safety levels of visited locations and browsed geosocial venues. We evaluate iSafe using crime and census data from the Miami-Dade (FL) county as well as data we collected from Yelp, a popular geosocial network.