An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the Hausdorff set distance in linear time for any Lp point distance
Information Processing Letters
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Communications of the ACM
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
A Critique of k-Anonymity and Some of Its Enhancements
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
Anonymizing moving objects: how to hide a MOB in a crowd?
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Privacy Preserving Publication of Moving Object Data
Privacy in Location-Based Applications
Walking in the crowd: anonymizing trajectory data for pattern analysis
Proceedings of the 18th ACM conference on Information and knowledge management
A prsimonious model of mobile partitioned networks with clustering
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Clustering of time series data-a survey
Pattern Recognition
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Discovering private trajectories using background information
Data & Knowledge Engineering
Privacy-aware location data publishing
ACM Transactions on Database Systems (TODS)
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Achieving Guaranteed Anonymity in GPS Traces via Uncertainty-Aware Path Cloaking
IEEE Transactions on Mobile Computing
Anonymization of moving objects databases by clustering and perturbation
Information Systems
Privacy-preserving publication of trajectories using microaggregation
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
MobiMix: Protecting location privacy with mix-zones over road networks
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
On the anonymity of periodic location samples
SPC'05 Proceedings of the Second international conference on Security in Pervasive Computing
On the privacy offered by (k, δ)-anonymity
Information Systems
Balancing trajectory privacy and data utility using a personalized anonymization model
Journal of Network and Computer Applications
Hi-index | 0.07 |
Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.