Abstract
Mobile users increasingly report their co-locations with other users, in addition to revealing their locations to online services. For instance, they tag the names of the friends they are with, in the messages and in the pictures they post on social networking websites. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users’ locations, thus further threatening their location privacy: as co-location information is taken into account, not only a user’s reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, with respect to an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose two polynomial-time approximate inference algorithms and we extensively evaluate their performance on a real dataset. Our experimental results show that, even in the case where the adversary considers co-locations with only a single friend of the targeted user, the location privacy of the user is decreased by up to 75% in a typical setting. Even in the case where a user does not disclose any location information, her privacy can decrease by up to 16% due to the information reported by other users.
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References
Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state markov chains. The Annals of Mathematical Statistics 37(6), 1554–1563 (1966)
How the NSA is tracking people right now (2013), http://apps.washingtonpost.com/g/page/national/how-the-nsa-is-tracking-people-right-now/634/ (last visited: February 2014)
Crandall, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. of the National Academy of Sciences (PNAS), 1–6 (2010)
Damiani, M.L., Bertino, E., Silvestri, C.: The PROBE framework for the personalized cloaking of private locations. Transactions on Data Privacy 3, 123–148 (2010)
De Mulder, Y., Danezis, G., Batina, L., Preneel, B.: Identification via location-profiling in GSM networks. In: WPES 2008: Proc. of the 7th ACM Workshop on Privacy in the Electronic Society, pp. 23–32 (2008)
Dey, R., Tang, C., Ross, K., Saxena, N.: Estimating age privacy leakage in online social networks. In: INFOCOM 2012: Proc. of the 31st Annual IEEE Int’l Conf. on Computer Communications, pp. 2836–2840 (2012)
Eagle, N., Pentland, A., Lazer, D.: Inferring Friendship Network Structure by Using Mobile Phone Data. Proc. of the National Academy of Sciences (PNAS) 106, 15274–15278 (2009)
Ghinita, G., Damiani, M.L., Silvestri, C., Bertino, E.: Preventing velocity-based linkage attacks in location-aware applications. In: GIS 2009: Proc. of the 17th ACM Int’l Symp. on Advances in Geographic Information Systems, pp. 246–255 (2009)
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)
Gymrek, M., McGuire, A.L., Golan, D., Halperin, E., Erlich, Y.: Identifying personal genomes by surname inference. Science 339(6117), 321–324 (2013)
Henne, B., Szongott, C., Smith, M.: Snapme if you can: Privacy threats of other peoples’ geo-tagged media and what we can do about it. In: WiSec 2013: Proc. of the 6th ACM Conf. on Security and Privacy in Wireless and Mobile Networks, pp. 95–106 (2013)
Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in trac-monitoring systems. IEEE Pervasive Computing 5(4), 38–46 (2006)
Huang, L., Yamane, H., Matsuura, K., Sezaki, K.: Silent cascade: Enhancing location privacy without communication QoS degradation. In: Clark, J.A., Paige, R.F., Polack, F.A.C., Brooke, P.J. (eds.) SPC 2006. LNCS, vol. 3934, pp. 165–180. Springer, Heidelberg (2006)
Humbert, M., Ayday, E., Hubaux, J.P., Telenti, A.: Addressing the concerns of the lacks family: Quantification of kin genomic privacy. In: CCS 2013: Proc. of the 20th ACM Conf. on Computer and Communications Security, pp. 1141–1152 (2013)
Krumm, J.: Inference attacks on location tracks. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 127–143. Springer, Heidelberg (2007)
Krumm, J.: A survey of computational location privacy. Personal Ubiquitous Computing 13(6), 391–399 (2009)
Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: Inferring user profiles in online social networks. In: WSDM 2010: Proc. of the 3rd ACM Int’l Conf. on Web Search and Data Mining, pp. 251–260 (2010)
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: S&P 2009: Proc. of the 30th IEEE Symp. on Security and Privacy, pp. 173–187 (2009)
Shokri, R., Theodorakopoulos, G., Danezis, G., Hubaux, J.-P., Le Boudec, J.-Y.: Quantifying location privacy: The case of sporadic location exposure. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 57–76. Springer, Heidelberg (2011)
Shokri, R., Theodorakopoulos, G., Le Boudec, J.Y., Hubaux, J.P.: Quantifying location privacy. In: S&P 2011: Proc. of the 32nd IEEE Symp. on Security and Privacy, pp. 247–262 (2011)
Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: Using social network as a side-channel. In: CCS 2012: Proc. of the 19th ACM Conf. on Computer and Communications Security, pp. 628–637 (2012)
Vicente, C., Freni, D., Bettini, C., Jensen, C.S.: Location-related privacy in geo-social networks. IEEE Internet Computing 15(3), 20–27 (2011)
Vratonjic, N., Huguenin, K., Bindschaedler, V., Hubaux, J.P.: How others compromise your location privacy: The case of shared public IPs at hotspots. In: De Cristofaro, E., Wright, M. (eds.) PETS 2013. LNCS, vol. 7981, pp. 123–142. Springer, Heidelberg (2013)
Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw GPS data for geographic applications on the web. In: WWW 2008: Proc. of the 17th ACM Int’l Conf. on World Wide Web, pp. 247–256 (2008)
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Olteanu, AM., Huguenin, K., Shokri, R., Hubaux, JP. (2014). Quantifying the Effect of Co-location Information on Location Privacy. In: De Cristofaro, E., Murdoch, S.J. (eds) Privacy Enhancing Technologies. PETS 2014. Lecture Notes in Computer Science, vol 8555. Springer, Cham. https://doi.org/10.1007/978-3-319-08506-7_10
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DOI: https://doi.org/10.1007/978-3-319-08506-7_10
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