Uniqueness Assessment of Human Mobility on Multi-Sensor Datasets

Abstract : The widespread adoption of handheld devices (e.g., smartphones, tablets) makes mobility traces of users broadly available to third party services. These traces are collected by means of various sensors embedded in the users’ devices, includ- ing GPS, WiFi and GSM. We study in this paper the mobility of 300 users over a period up to 31 months from the perspective of the above three types of data and with a focus on two cities, i.e., Lausanne (Switzerland) and Lyon (France). We found that users’ mobility traces, no matter if they are collected using GPS, WiFi or GSM antennas, are highly unique. We show that on average only four spatio-temporal points from the WiFi, GSM and GPS traces are enough to uniquely identify 94% of the individuals, on both datasets. In addition, we show that using the temporal dimension (i.e., whether users move or are in a meaningful location such as the home or the working place) drastically improves the capacity to uniquely identify users compared to when only exploiting the spatial dimension (by 14% on average). In some cases, using the temporal dimension alone can represent a better mobility footprint than the spatial dimension to discriminate users. We further conduct a de-anonymisation attack to assess how mobility traces can be re-identified, and show that almost all users can be de-anonymised with a high success rate. Finally, we apply different location privacy protection mechanisms (LPPMs), applying spatial filtering, temporal cloaking, adding spatial noise to mobility data, or using generalisation, and analyse the impact of these mechanisms on both the uniqueness of users’ mobility traces and the outcome of the de-anonymisation attack. We show that spatially obfuscating mobility data is not enough to protect users, and that classical LPPMs are not able to protect users against a de-anonymisation attack. We finally conclude this paper by drawing some insights towards future spatio-temporal LPPMs.
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[Research Report] LIRIS UMR CNRS 5205. 2016
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Soumis le : jeudi 8 décembre 2016 - 23:20:30
Dernière modification le : samedi 10 décembre 2016 - 01:05:14
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  • HAL Id : hal-01381986, version 1



Antoine Boutet, Sonia Ben Mokhtar, Vincent Primault. Uniqueness Assessment of Human Mobility on Multi-Sensor Datasets. [Research Report] LIRIS UMR CNRS 5205. 2016. 〈hal-01381986〉



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