{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:54:47Z","timestamp":1780635287304,"version":"3.54.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030659646","type":"print"},{"value":"9783030659653","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-65965-3_34","type":"book-chapter","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T19:48:52Z","timestamp":1612208932000},"page":"501-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks"],"prefix":"10.1007","author":[{"given":"Francesca","family":"Naretto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Pellungrini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franco Maria","family":"Nardini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fosca","family":"Giannotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Altch\u00e9, 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\u2013359 (2017)","DOI":"10.1109\/ITSC.2017.8317913"},{"issue":"2","key":"34_CR2","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/TVCG.2010.44","volume":"17","author":"NV Andrienko","year":"2011","unstructured":"Andrienko, N.V., Andrienko, G.L.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Vis. Comput. Graph. 17(2), 205\u2013219 (2011)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"2","key":"34_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.4018\/IJSSE.2015040104","volume":"6","author":"A Armando","year":"2015","unstructured":"Armando, A., Bezzi, M., Metoui, N., Sabetta, A.: Risk-based privacy-aware information disclosure. Int. J. Secur. Softw. Eng. 6(2), 70\u201389 (2015)","journal-title":"Int. J. Secur. Softw. Eng."},{"key":"34_CR4","unstructured":"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)"},{"key":"34_CR5","unstructured":"Craven, M., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: NIPS, pp. 24\u201330 (1996)"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: JMLR, pp. 37\u201345. Elsevier (1994)","DOI":"10.1016\/B978-1-55860-335-6.50013-1"},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"349","DOI":"10.3390\/su12010349","volume":"12","author":"A Crivellari","year":"2020","unstructured":"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","journal-title":"Sustainability"},{"issue":"4","key":"34_CR8","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s41060-018-0144-8","volume":"7","author":"H Deng","year":"2019","unstructured":"Deng, H.: Interpreting tree ensembles with intrees. Int. J. Data Sci. Anal. 7(4), 277\u2013287 (2019)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"6","key":"34_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MIS.2019.2957223","volume":"34","author":"R Guidotti","year":"2019","unstructured":"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\u201323 (2019)","journal-title":"IEEE Intell. Syst."},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2019","unstructured":"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\u201342 (2019)","journal-title":"ACM Comput. Surv."},{"issue":"8","key":"34_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"34_CR12","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: NIPS, pp. 4765\u20134774 (2017)"},{"key":"34_CR13","series-title":"Patterns & Practices","volume-title":"Improving Web Application Security: Threats and Countermeasures","author":"J Meier","year":"2003","unstructured":"Meier, J., Corporation, M.: Improving Web Application Security: Threats and Countermeasures. Patterns & Practices. Microsoft, Redmond (2003)"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Mohammed, N., Fung, B.C., Debbabi, M.: Walking in the crowd: anonymizing trajectory data for pattern analysis. In: CIKM, pp. 1441\u20131444. ACM (2009)","DOI":"10.1145\/1645953.1646140"},{"issue":"2","key":"34_CR15","first-page":"91","volume":"3","author":"A Monreale","year":"2010","unstructured":"Monreale, A., et al.: Movement data anonymity through generalization. TDP 3(2), 91\u2013121 (2010)","journal-title":"TDP"},{"key":"34_CR16","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1038\/srep01376","volume":"3","author":"YA de Montjoye","year":"2013","unstructured":"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)","journal-title":"Sci. Rep."},{"issue":"3","key":"34_CR17","first-page":"31:1","volume":"9","author":"R Pellungrini","year":"2018","unstructured":"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\u201331:27 (2018)","journal-title":"ACM TIST"},{"issue":"2","key":"34_CR18","first-page":"139","volume":"11","author":"F Pratesi","year":"2018","unstructured":"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\u2013167 (2018)","journal-title":"Trans. Data Priv."},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: ACM SIGKDD, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Rossi, L., Musolesi, M.: It\u2019s the way you check-in: identifying users in location-based social networks. In: COSN, pp. 215\u2013226. ACM (2014)","DOI":"10.1145\/2660460.2660485"},{"issue":"28","key":"34_CR21","first-page":"307","volume":"2","author":"LS Shapley","year":"1953","unstructured":"Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307\u2013317 (1953)","journal-title":"Contrib. Theory Games"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"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\u20138 (2018)","DOI":"10.1109\/IJCNN.2018.8489734"},{"key":"34_CR23","unstructured":"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)"},{"key":"34_CR24","unstructured":"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\u20132624. AAAI Press (2016)"},{"key":"34_CR25","unstructured":"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\u201324 (2014)"},{"key":"34_CR26","series-title":"Studies in Big Data","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57358-8","volume-title":"Data Privacy: Foundations, New Developments and the Big Data Challenge","author":"V Torra","year":"2017","unstructured":"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"},{"issue":"2","key":"34_CR27","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/a10020037","volume":"10","author":"F Wu","year":"2017","unstructured":"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","journal-title":"Algorithms"}],"container-title":["Communications in Computer and Information Science","ECML PKDD 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65965-3_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:40:41Z","timestamp":1619264441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65965-3_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030659646","9783030659653"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65965-3_34","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"945","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}