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However, due to the sensitive nature of this data, accurately identifying privacy risks is essential before deciding to release it to the public. Recent work has proposed the use of machine learning models for predicting privacy risk on raw mobility trajectories and the use of <jats:sc>shap<\/jats:sc> for risk explanation. However, applying <jats:sc>shap<\/jats:sc> to mobility data results in explanations that are of limited use both for privacy experts and end-users. In this work, we present a novel version of the <jats:sc>Expert<\/jats:sc> privacy risk prediction and explanation framework specifically tailored for human mobility data. We leverage state-of-the-art algorithms in time series classification, as <jats:sc>Rocket<\/jats:sc> and <jats:sc>InceptionTime<\/jats:sc>, to improve risk prediction while reducing computation time. Additionally, we address two key issues with <jats:sc>shap<\/jats:sc> explanation on mobility data: first, we devise an entropy-based mask to efficiently compute <jats:sc>shap<\/jats:sc> values for privacy risk in mobility data; second, we develop a module for interactive analysis and visualization of <jats:sc>shap<\/jats:sc> values over a map, empowering users with an intuitive understanding of <jats:sc>shap<\/jats:sc> values and privacy risk.<\/jats:p>","DOI":"10.1007\/978-3-031-45275-8_22","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T06:01:56Z","timestamp":1696658516000},"page":"325-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EXPHLOT: EXplainable Privacy Assessment for\u00a0Human LOcation Trajectories"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-7787","authenticated-orcid":false,"given":"Francesca","family":"Naretto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3268-9271","authenticated-orcid":false,"given":"Roberto","family":"Pellungrini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4404-4147","authenticated-orcid":false,"given":"Salvatore","family":"Rinzivillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0051-0604","authenticated-orcid":false,"given":"Daniele","family":"Fadda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Altch\u00e9, F., de La Fortelle, A.: An LSTM network for highway trajectory prediction. 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