00601nas a2200133   4500008004100000020002200041245013300063210006900196260001200265100001900277700001900296700002500315856012700340       2024                        eng d  a978-989-758-709-200aEnhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection0 aEnhancing Privacy and Utility in Federated Learning A Hybrid P2P  bINSTICC1 aCorbucci, Luca1 aMonreale, Anna1 aPellungrini, Roberto  uhttps://kdd.isti.cnr.it/publications/enhancing-privacy-and-utility-federated-learning-hybrid-p2p-and-server-based-approach00505nas a2200121   4500008004100000245007600041210006900117100002300186700002500209700001900234700002200253856010800275       2023                        eng d00aEXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories0 aEXPHLOT EXplainable Privacy assessment for Human LOcation Trajec1 aNaretto, Francesca1 aPellungrini, Roberto1 aFadda, Daniele1 aRinzivillo, Salvo  uhttps://kdd.isti.cnr.it/publications/exphlot-explainable-privacy-assessment-human-location-trajectories01545nas a2200169   4500008004100000020001400041245006400055210006400119260000900183300001100192520103400203100002501237700002101262700001501283700001901298856005801317       2020                        eng d  a1558-001600aModeling Adversarial Behavior Against Mobility Data Privacy0 aModeling Adversarial Behavior Against Mobility Data Privacy  c2020  a1 - 143 aPrivacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.1 aPellungrini, Roberto1 aPappalardo, Luca1 aSimini, F.1 aMonreale, Anna  uhttps://ieeexplore.ieee.org/abstract/document/919989302034nas a2200217   4500008004100000020002200041245006900063210006900132260005200201520129400253100002301547700002501570700001901595700002701614700002001641700002101661700002501682700002501707700001701732856006701749       2020                        eng d  a978-3-030-61527-700aPredicting and Explaining Privacy Risk Exposure in Mobility Data0 aPredicting and Explaining Privacy Risk Exposure in Mobility Data  aChambSpringer International Publishingc2020//3 aMobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.1 aNaretto, Francesca1 aPellungrini, Roberto1 aMonreale, Anna1 aNardini, Franco, Maria1 aMusolesi, Mirco1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_2702783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe 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.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3401227nas a2200133   4500008004100000245007400041210006900115520077700184100002100961700002200982700002501004700002001029856004401049       2019                        eng d00aHuman Mobility from theory to practice: Data, Models and Applications0 aHuman Mobility from theory to practice Data Models and Applicati3 aThe inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.1 aPappalardo, Luca1 aBarlacchi, Gianni1 aPellungrini, Roberto1 aSimini, Filippo  uhttps://doi.org/10.1145/3308560.332009901650nas a2200301   4500008004100000020002200041245004800063210004800111260005200159520072600211100002500937700001900962700002300981700001901004700001901023700001901042700002101061700002001082700002201102700002101124700002301145700002101168700002401189700002301213700002201236700002301258856006701281       2019                        eng d  a978-3-030-13463-100aPrivacy Risk for Individual Basket Patterns0 aPrivacy Risk for Individual Basket Patterns  aChambSpringer International Publishingc2019//3 aRetail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.1 aPellungrini, Roberto1 aMonreale, Anna1 aGuidotti, Riccardo1 aAlzate, Carlos1 aMonreale, Anna1 aBioglio, Livio1 aBitetta, Valerio1 aBordino, Ilaria1 aCaldarelli, Guido1 aFerretti, Andrea1 aGuidotti, Riccardo1 aGullo, Francesco1 aPascolutti, Stefano1 aPensa, Ruggero, G.1 aRobardet, Céline1 aSquartini, Tiziano  uhttps://link.springer.com/chapter/10.1007/978-3-030-13463-1_1101262nas a2200133   4500008004100000245005000041210005000091520085000141100002500991700002101016700002301037700001901060856004901079       2018                        eng d00aAnalyzing Privacy Risk in Human Mobility Data0 aAnalyzing Privacy Risk in Human Mobility Data3 aMobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://doi.org/10.1007/978-3-030-04771-9_1000999nas a2200121   4500008004100000245004200041210004200083520063500125100002500760700002300785700002100808856004800829       2017                        eng d00aAssessing Privacy Risk in Retail Data0 aAssessing Privacy Risk in Retail Data3 aRetail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.1 aPellungrini, Roberto1 aPratesi, Francesca1 aPappalardo, Luca  uhttps://doi.org/10.1007/978-3-319-71970-2_301645nas a2200169   4500008004100000022001400041245007300055210006900128300001700197490000600214520112800220100002501348700002101373700002301394700001901417856003901436       2017                        eng d  a2157-690400aA Data Mining Approach to Assess Privacy Risk in Human Mobility Data0 aData Mining Approach to Assess Privacy Risk in Human Mobility Da  a31:1–31:270 v93 aHuman mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttp://doi.acm.org/10.1145/310677401483nas a2200145   4500008004100000020002200041245005900063210005900122520097800181100002501159700002101184700002301205700001901228856009001247       2017                        eng d  a978-3-319-66283-100aFast Estimation of Privacy Risk in Human Mobility Data0 aFast Estimation of Privacy Risk in Human Mobility Data3 aMobility 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. 1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/fast-estimation-privacy-risk-human-mobility-data