Cost and Imprecision in Modeling the Position of Moving Objects
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Protection of Location Privacy using Dummies for Location-based Services
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
High-Assurance Integrity Techniques for Databases
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
An Approach to Evaluate Data Trustworthiness Based on Data Provenance
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Protecting Privacy in Continuous Location-Tracking Applications
IEEE Security and Privacy
Assignment Problems
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Assessing the trustworthiness of location data based on provenance
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Assessing the trustworthiness of location data corresponding to individuals is essential in several applications, such as forensic science and epidemic control. To obtain accurate and trustworthy location data, analysts must often gather and correlate information from several independent sources, e.g., physical observation, witness testimony, surveillance footage, etc. However, such information may be fraudulent, its accuracy may be low, and its volume may be insufficient to ensure highly trustworthy data. On the other hand, recent advancements in mobile computing and positioning systems, e.g., GPS-enabled cell phones, highway sensors, etc., bring new and effective technological means to track the location of an individual. Nevertheless, collection and sharing of such data must be done in ways that do not violate an individual's right to personal privacy. Previous research efforts acknowledged the importance of assessing location data trustworthiness, but they assume that data is available to the analyst in direct, unperturbed form. However, such an assumption is not realistic, due to the fact that repositories of personal location data must conform to privacy regulations. In this paper, we study the challenging problem of refining trustworthiness of location data with the help of large repositories of anonymized information. We show how two important trustworthiness evaluation techniques, namely common pattern analysis and conflict/support analysis, can benefit from the use of anonymized location data. We have implemented a prototype of the proposed privacy-preserving trustworthiness evaluation techniques, and the experimental results demonstrate that using anonymized data can significantly help in improving the accuracy of location trustworthiness assessment.