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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 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.
References
Altché, F., de La Fortelle, A.: An LSTM network for highway trajectory prediction. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 353–359 (2017)
Andrienko, N.V., Andrienko, G.L.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Vis. Comput. Graph. 17(2), 205–219 (2011)
Armando, A., Bezzi, M., Metoui, N., Sabetta, A.: Risk-based privacy-aware information disclosure. Int. J. Secur. Softw. Eng. 6(2), 70–89 (2015)
Cavoukian, A., Emam, K.: Dispelling the myths surrounding de-identification: anonymization remains a strong tool for protecting privacy. DesLibris: Documents collection, Information and Privacy Commissioner of Ontario, Canada (2011)
Craven, M., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: NIPS, pp. 24–30 (1996)
Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: JMLR, pp. 37–45. Elsevier (1994)
Crivellari, A., Beinat, E.: LSTM-based deep learning model for predicting individual mobility traces of short-term foreign tourists. Sustainability 12, 349 (2020). https://doi.org/10.3390/su12010349
Deng, H.: Interpreting tree ensembles with intrees. Int. J. Data Sci. Anal. 7(4), 277–287 (2019)
Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14–23 (2019)
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51, 1–42 (2019)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: NIPS, pp. 4765–4774 (2017)
Meier, J., Corporation, M.: Improving Web Application Security: Threats and Countermeasures. Patterns & Practices. Microsoft, Redmond (2003)
Mohammed, N., Fung, B.C., Debbabi, M.: Walking in the crowd: anonymizing trajectory data for pattern analysis. In: CIKM, pp. 1441–1444. ACM (2009)
Monreale, A., et al.: Movement data anonymity through generalization. TDP 3(2), 91–121 (2010)
de Montjoye, Y.A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1376 (2013)
Pellungrini, R., Pappalardo, L., Pratesi, F., Monreale, A.: A data mining approach to assess privacy risk in human mobility data. ACM TIST 9(3), 31:1–31:27 (2018)
Pratesi, F., Monreale, A., Trasarti, R., Giannotti, F., Pedreschi, D., Yanagihara, T.: Prudence: a system for assessing privacy risk vs utility in data sharing ecosystems. Trans. Data Priv. 11(2), 139–167 (2018)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: explaining the predictions of any classifier. In: ACM SIGKDD, pp. 1135–1144 (2016)
Rossi, L., Musolesi, M.: It’s the way you check-in: identifying users in location-based social networks. In: COSN, pp. 215–226. ACM (2014)
Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)
Shi, Z., Xu, M., Pan, Q., Yan, B., Zhang, H.: LSTM-based flight trajectory prediction. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2018)
Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70 (2017)
Song, X., Kanasugi, H., Shibasaki, R.: Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 2618–2624. AAAI Press (2016)
Song, Y., Dahlmeier, D., Bressan, S.: Not so unique in the crowd: a simple and effective algorithm for anonymizing location data. In: International Workshop on Privacy-Preserving IR: When Information Retrieval Meets Privacy and Security, pp. 19–24 (2014)
Torra, V.: Data Privacy: Foundations, New Developments and the Big Data Challenge. SBD, vol. 28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57358-8
Wu, F., Fu, K., Wang, Y., Xiao, Z., Fu, X.: A spatial-temporal-semantic neural network algorithm for location prediction on moving objects. Algorithms 10(2), 37 (2017). https://doi.org/10.3390/a10020037
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-65965-3_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65964-6
Online ISBN: 978-3-030-65965-3
eBook Packages: Computer ScienceComputer Science (R0)
