Abstract
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.
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- 2.
The Python code for attacks simulation is available here: https://github.com/pellungrobe/privacy-mobility-lib.
- 3.
We use the Python package scikit-learn to perform the regression experiments.
- 4.
For a given type of attack we report the sum of the execution times of the attacks for configurations \(k=2, 3, 4, 5\). We perform the experiments on Ubuntu 16.04.1 LTS 64 bit, 32 GB RAM, 3.30 GHz Intel Core i7.
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Funded by the European project SoBigData (Grant Agreement 654024).
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Pellungrini, R., Pappalardo, L., Pratesi, F., Monreale, A. (2017). Fast Estimation of Privacy Risk in Human Mobility Data. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security . SAFECOMP 2017. Lecture Notes in Computer Science(), vol 10489. Springer, Cham. https://doi.org/10.1007/978-3-319-66284-8_35
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