TY - JOUR
T1 - CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects
JF - Transaction in GIS
Y1 - 2013
A1 - Vania Bogorny
A1 - Chiara Renso
A1 - Artur Ribeiro de Aquino
A1 - Fernando de Lucca Siqueira
A1 - Luis Otavio Alvares
ER -
TY - JOUR
T1 - Semantic Trajectories Modeling and Analysis
JF - ACM Computing Surveys
Y1 - 2013
A1 - Christine Parent
A1 - Stefano Spaccapietra
A1 - Chiara Renso
A1 - Gennady Andrienko
A1 - Natalia Andrienko
A1 - Vania Bogorny
A1 - Damiani M L,
A1 - Gkoulalas-Divanis A,
A1 - de José Antônio Fernandes Macêdo
A1 - Nikos Pelekis
VL - 45
ER -
TY - CONF
T1 - Where Have You Been Today? Annotating Trajectories with DayTag
T2 - International Conference on Spatial and Spatio-temporal Databases (SSTD)
Y1 - 2013
A1 - S Rinzivillo
A1 - Fernando de Lucca Siqueira
A1 - Lorenzo Gabrielli
A1 - Chiara Renso
A1 - Vania Bogorny
JF - International Conference on Spatial and Spatio-temporal Databases (SSTD)
ER -
TY - JOUR
T1 - C-safety: a framework for the anonymization of semantic trajectories
JF - Transactions on Data Privacy
Y1 - 2011
A1 - Anna Monreale
A1 - Roberto Trasarti
A1 - Dino Pedreschi
A1 - Chiara Renso
A1 - Vania Bogorny
AB - 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.
VL - 4
UR - http://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=35994039
ER -
TY - CONF
T1 - Preserving privacy in semantic-rich trajectories of human mobility
T2 - SPRINGL
Y1 - 2010
A1 - Anna Monreale
A1 - Roberto Trasarti
A1 - Chiara Renso
A1 - Dino Pedreschi
A1 - Vania Bogorny
AB - 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.
JF - SPRINGL
ER -
TY - CHAP
T1 - Knowledge Discovery from Geographical Data
T2 - Mobility, Data Mining and Privacy
Y1 - 2008
A1 - S Rinzivillo
A1 - Franco Turini
A1 - Vania Bogorny
A1 - Christine Körner
A1 - Bart Kuijpers
A1 - Michael May
JF - Mobility, Data Mining and Privacy
ER -