Abstract
The evolution and expansion of location tracking technologies such as GPS, RFID, etc. and their integration with handheld devices, created a new trend of services and applications based on location information. However, location data is sensible data that could seriously compromise the privacy of the individuals. There is a large body of research in the area of location privacy, where researchers try to tackle this privacy problem. In this article we describe one of the systems implemented in the ARES project to anonymize trajectories of cars in a prototype, following an approach based on time series.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghinita, G.: Private queries and trajectory anonymization: a dual perspective on location privacy. Trans. Data Priv. 2, 3–19 (2009), http://www.tdp.cat/issues/tdp.a018a09.pdf
Lin, D., Bertino, E., Cheng, R., Prabhakar, S.: Location privacy in moving-object environments. Trans. Data Priv. 2, 21–46 (2009). http://www.tdp.cat/issues/tdp.a019a09.pdf
Nergiz, M.E., Atzori, M., Güç, B., Saygın, Y.: Towards trajectory anonymization: a generalization-based approach. Trans. Data Priv. 2(1), 47–75 (2009)
Poolsappasit, N., Ray, I.: Towards achieving personalized privacy for location-based services. Trans. Data Priv. 2(1), 77–99 (2009). http://dl.acm.org/citation.cfm?id=1556406.1556411
Damiani, M.L.: Third party geolocation services in lbs: Privacy requirements and research issues. Trans. Data Priv 4(2), 55–72 (2011). http://dl.acm.org/citation.cfm?id=2019316.2019318
Ho, S.S., Ruan, S.: Preserving privacy for interesting location pattern mining from trajectory data. Trans. Data Priv. 6, 185–198 (2013)
Martinez-Bea, S., Torra, V.: Trajectory anonymization from a time series perspective. In: Fuzzy Systems (FUZZ), 2011 IEEE International Conference on. pp. 401–408. IEEE (2011)
Nin, J., Torra, V.: Towards the evaluation of time series protection methods. Inf. Sci. 179(11), 1663–1677 (2009)
Domingo-Ferrer, J., Sramka, M., Trujillo-Rasúa, R.: Privacy-preserving publication of trajectories using microaggregation. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS. pp. 26–33. SPRINGL ’10, ACM, New York, NY, USA (2010), http://doi.acm.org/10.1145/1868470.1868478
Torra, V., Domingo-Ferrer, J.: Record linkage methods for multidatabase data mining. In: Torra, V. (ed.) Information Fusion in Data Mining, Studies in Fuzziness and Soft Computing, vol. 123, pp. 101–132. Springer, Berlin (2003)
Winkler, W.E.: Re-identification methods for masked microdata. In: Proceeding of the Privacy in Statistical Databases 2004, Springer LNCS 3050. pp. 216–230. Springer (2004)
Domingo-Ferrer, J., Torra, V.: Validating distance-based record linkage with probabilistic record linkage. In: Escrig, M., Toledo, F., Golobardes, E. (eds.) Topics in Artificial Intelligence. Lecture Notes in Computer Science, vol. 2504, pp. 207–215. Springer, Berlin (2002)
Martínez-Bea, S., Torra, V.: An evaluation framework for location privacy. CCIA 2, 140–148 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Martínez-Bea, S. (2015). A Prototype for Anonymizing Trajectories from a Time Series Perspective. In: Navarro-Arribas, G., Torra, V. (eds) Advanced Research in Data Privacy. Studies in Computational Intelligence, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-319-09885-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-09885-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09884-5
Online ISBN: 978-3-319-09885-2
eBook Packages: EngineeringEngineering (R0)