This paper sheds light on the trade-offs between privacy and utility in mobility-based geographic datasets. We aim at finding out whether it is possible to protect the privacy of the users in a dataset while, at the same time, maintaining intact the utility of the information that it contains. In particular, we focus on geo-indistinguishability as a privacy-preserving sanitization methodology, and we evaluate its effects on the utility of the Geolife dataset. We test the sanitized dataset in two real world scenarios: 1. Deploying an infrastructure of WiFi hotspots to offload the mobile traffic of users living, working, or commuting in a wide geographic area; 2. Simulating the spreading of a gossip-based epidemic as the outcome of a device-to-device communication protocol. We show the extent to which the current geo-indistinguishability techniques trade privacy for utility in real world applications and we focus on their effects at the levels of the population as a whole and of single individuals.
Catch me if you can: how Geo-indistinguishability affects utility in mobility-based geographic datasets / Di Luzio, A.; Dikov, G.; Viana, A. C.; Palamidessi, C.; Chatzikokolakis, K.; Stefa, J.. - (2019), pp. 1-10. (Intervento presentato al convegno 3rd ACM SIGSPATIAL International Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising, LocalRec 2019, held in conjunction with the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2019 tenutosi a Chicago; USA) [10.1145/3356994.3365498].
Catch me if you can: how Geo-indistinguishability affects utility in mobility-based geographic datasets
Stefa J.
2019
Abstract
This paper sheds light on the trade-offs between privacy and utility in mobility-based geographic datasets. We aim at finding out whether it is possible to protect the privacy of the users in a dataset while, at the same time, maintaining intact the utility of the information that it contains. In particular, we focus on geo-indistinguishability as a privacy-preserving sanitization methodology, and we evaluate its effects on the utility of the Geolife dataset. We test the sanitized dataset in two real world scenarios: 1. Deploying an infrastructure of WiFi hotspots to offload the mobile traffic of users living, working, or commuting in a wide geographic area; 2. Simulating the spreading of a gossip-based epidemic as the outcome of a device-to-device communication protocol. We show the extent to which the current geo-indistinguishability techniques trade privacy for utility in real world applications and we focus on their effects at the levels of the population as a whole and of single individuals.File | Dimensione | Formato | |
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