Quantifying Privacy Loss of Human Mobility Graph Topology
Publication Date
2018-06-01Journal Title
Proceedings on Privacy Enhancing Technologies
ISSN
2299-0984
Publisher
Privacy Enhancing Technologies Symposium Advisory Board
Volume
2018
Issue
3
Pages
5-21
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Manousakas, D., Mascolo, C., Beresford, A. R., Chan, D., & Sharma, N. (2018). Quantifying Privacy Loss of Human Mobility Graph Topology. Proceedings on Privacy Enhancing Technologies, 2018 (3), 5-21. https://doi.org/10.1515/popets-2018-0018
Abstract
<jats:title>Abstract</jats:title>
<jats:p> Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.</jats:p>
Sponsorship
Alan Turing Institute (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1515/popets-2018-0018
This record's URL: https://www.repository.cam.ac.uk/handle/1810/276612
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
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