Journal article

Uniqueness in the City: Urban Morphology and Location Privacy

H Cao, J Feng, Y Li, V Kostakos

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | Association for Computing Machinery | Published : 2018


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 contri..

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