<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Artur Ribeiro de Aquino</style></author><author><style face="normal" font="default" size="100%">Fernando de Lucca Siqueira</style></author><author><style face="normal" font="default" size="100%">Luis Otavio Alvares</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects </style></title><secondary-title><style face="normal" font="default" size="100%">Transaction in GIS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Parent</style></author><author><style face="normal" font="default" size="100%">Stefano Spaccapietra</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Gennady Andrienko</style></author><author><style face="normal" font="default" size="100%">Natalia Andrienko</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author><author><style face="normal" font="default" size="100%">Damiani M L,</style></author><author><style face="normal" font="default" size="100%">Gkoulalas-Divanis A,</style></author><author><style face="normal" font="default" size="100%">de José Antônio Fernandes Macêdo</style></author><author><style face="normal" font="default" size="100%">Nikos Pelekis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Trajectories Modeling and Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Computing Surveys</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August 2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">45</style></volume></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Fernando de Lucca Siqueira</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Where Have You Been Today? Annotating Trajectories with DayTag</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Spatial and Spatio-temporal Databases (SSTD)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">467-471</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">C-safety: a framework for the anonymization of semantic trajectories</style></title><secondary-title><style face="normal" font="default" size="100%">Transactions on Data Privacy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=2019319&amp;CFID=803961971&amp;CFTOKEN=35994039</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">73-101</style></pages><abstract><style face="normal" font="default" size="100%">The increasing abundance of data about the trajectories of personal movement is opening
new opportunities for analyzing and mining human mobility. However, new risks emerge since it
opens new ways of intruding into personal privacy. Representing the personal movements as sequences
of places visited by a person during her/his movements - semantic trajectory - poses great
privacy threats. In this paper we propose a privacy model defining the attack model of semantic trajectory
linking and a privacy notion, called c-safety based on a generalization of visited places based
on a taxonomy. This method provides an upper bound to the probability of inferring that a given
person, observed in a sequence of non-sensitive places, has also visited any sensitive location. Coherently
with the privacy model, we propose an algorithm for transforming any dataset of semantic
trajectories into a c-safe one. We report a study on two real-life GPS trajectory datasets to show how
our algorithm preserves interesting quality/utility measures of the original trajectories, when mining
semantic trajectories sequential pattern mining results. We also empirically measure how the
probability that the attacker’s inference succeeds is much lower than the theoretical upper bound
established.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preserving privacy in semantic-rich trajectories of human mobility</style></title><secondary-title><style face="normal" font="default" size="100%">SPRINGL</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">47-54</style></pages><abstract><style face="normal" font="default" size="100%">The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Vania Bogorny</style></author><author><style face="normal" font="default" size="100%">Christine Körner</style></author><author><style face="normal" font="default" size="100%">Bart Kuijpers</style></author><author><style face="normal" font="default" size="100%">Michael May</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Discovery from Geographical Data</style></title><secondary-title><style face="normal" font="default" size="100%">Mobility, Data Mining and Privacy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">243-265</style></pages></record></records></xml>