Abstract
The analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.
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- 1.
Voronoi tessellation obtained using http://geoanalytics.net/V-Analytics.
- 2.
Code available on https://github.com/francescanaretto/Privacy-Risk-onMobility-Data-with-LSTMs.
- 3.
The analysis of the Lstm has been performed with the see-rnn package: https://github.com/OverLordGoldDragon/see-rnn.
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Acknowledgments
This work has been partially funded by the European projects SoBigData-PlusPlus (Grant Agreement 871042), XAI (Grant Agreement 834756) and HumanE-AI-Net (Grant Agreement 952026).
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Naretto, F., Pellungrini, R., Nardini, F.M., Giannotti, F. (2020). Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_34
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DOI: https://doi.org/10.1007/978-3-030-65965-3_34
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