Affiliation:
1. Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China
2. University of Melbourne, Australia
Abstract
We investigate the potential for privacy leaks when users reveal their nearby Points-of-Interest (POIs). Specifically, we investigate whether and how a person's location can be reverse-engineered when that person simply reveals their nearby POI types (e.g. 2 schools and 3 restaurants). We approach our analysis by introducing a "Location Re-identification" algorithm that is computationally efficient. Using data from Open Street Map, we conduct our analysis on datasets of multiple representative cities: New York City, Melbourne, Vancouver, Zurich and Shanghai. Our analysis indicates that urban morphology has a clear link to location privacy, and highlights a number of urban factors that contribute to location privacy. Our findings can be used in any systems or platforms where users reveal their proximal POIs, such as recommendation systems, advertising platforms, and appstores.
Funder
Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
National Nature Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Cited by
16 articles.
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