Abstract
To ensure privacy for route planning applications and other location based services (LBS), the service provider must be prevented from tracking a user’s path during navigation on the application level. However, the navigation functionality must be preserved. We introduce the algorithm PARTS to split route requests into route parts which will be submitted to an LBS in an unlinkable way. Equipped with the usage of dummy requests and time shifting, our approach can achieve better privacy. We will show that our algorithm protects privacy in the presence of a realistic adversary model while maintaining the service quality.
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
Similar content being viewed by others
Notes
- 1.
The straight line distance is used since it is locally computable and does not require any additional requests to a third party, thus not leaking any information.
- 2.
Since our users are exclusively moving within a city, we use the simplified assumption that travel speed is constant per road segment (homogeneous flow). Hereby, we use values from 37.5 kph to 62.5 kph. The adversary only knows that people will respect traffic regulations, therefore he uses a constant value of 50 kph for the whole route.
References
Ağır, B., Huguenin, K., Hengartner, U., Hubaux, J.P.: On the privacy implications of location semantics. In: Proceedings on Privacy Enhancing Technologies 2016, vol. 4, p. 1 (2016)
Bindschaedler, V., Shokri, R.: Synthesizing plausible privacy-preserving location traces. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 546–563. IEEE (2016)
Golle, P., Partridge, K.: On the anonymity of home/work location pairs. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 390–397. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01516-8_26
Hughes, N.: Inside ios 9: Apple’s maps app. gets smarter with automatic directions based on user habits. Apple Insider (2015)
Kramer, A.D.I., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. 111(24), 8788–8790 (2014)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discovery Data (TKDD) 1(1), 3 (2007)
Michalevsky, Y., Schulman, A., Veerapandian, G.A., Boneh, D., Nakibly, G.: PowerSpy: location tracking using mobile device power analysis. In: 24th USENIX Security Symposium (USENIX Security 2015), pp. 785–800 (2015)
Palanisamy, B., Liu, L.: MobiMix: protecting location privacy with mix-zones over road networks. In: IEEE 27th International Conference on Data Engineering (ICDE 2011), pp. 494–505. IEEE, Piscataway (2011)
Shokri, R., Troncoso, C., Diaz, C., Freudiger, J., Hubaux, J.P.: Unraveling an old cloak: k-anonymity for location privacy. In: Al-Shaer, E., Frikken, K. (eds.) Proceedings of the 9th Annual ACM Workshop on Privacy in the Electronic Society, p. 115. ACM, New York (2010)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Wang, T., Liu, L.: Privacy-aware mobile services over road networks. Proc. VLDB Endowment 2(1), 1042–1053 (2009)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33, 32–39 (2010)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Roth, C., Hartmann, L., Kesdoğan, D. (2017). PARTS – Privacy-Aware Routing with Transportation Subgraphs. In: Lipmaa, H., Mitrokotsa, A., Matulevičius, R. (eds) Secure IT Systems. NordSec 2017. Lecture Notes in Computer Science(), vol 10674. Springer, Cham. https://doi.org/10.1007/978-3-319-70290-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-70290-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70289-6
Online ISBN: 978-3-319-70290-2
eBook Packages: Computer ScienceComputer Science (R0)