Abstract
Portable devices today are striding computation potential and memory at par and sometimes even significantly higher than those found in desktop machines. Today’s lifestyle is more mobile than earlier, offices are open cafe houses and people prefer to work from plug and play office spaces. With the exponential growth of mobile technology and its users, a more astute system is in place called ‘location based services’ (LBSs). Since client uses these services out of their dynamic or static working behavior and is required to submit the real location (with query) to get the absolute benefits, it is crucial to layout the systems and frameworks which can protect users from the security and privacy threats by keeping the location information private. Existing defense mechanisms based on trusted third party possesses the significant vulnerability to vicinity identification, which in turn end up identifying the real world identity of the query issuer. In this paper, we present a scheme to preserve location privacy, called VIC-PRO, that fortifies the location privacy of the client alongside vicinity protection using geometrical transformations. We experimentally compare the scheme with other existing mechanisms to demonstrate that VIC-PRO is more efficient, safe against vicinity identification attack, and guarantees better user privacy in an LBS setup.
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References
Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2007). GNSS global navigation satellite systems: GPS, GLONASS, Galileo, and more. Berlin: Springer.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557–570.
Samarati, P. (2001). Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering, 13(6), 1010–1027.
Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 571–588.
Gupta, R. (2017). An exploration to location based service and its privacy preserving techniques: A survey. Wireles Personal Communications, 96(04), 1973–2007.
Gruteser, M., & Grunwald, D. (2003). Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st international conference on mobile systems, applications and services (pp. 31–42). ACM.
Mokbel, M. F., Chow, C.-Y., & Aref W. G. (2006). The new Casper: Query processing for location services without compromising privacy. In Proceedings of the 32nd international conference on very large data bases (pp. 763–774). VLDB Endowment.
Mokbel, M. F. (2007). Privacy in location-based services: State-of-the-art and research directions. In International conference on mobile data management, 2007 (pp. 228–228). IEEE.
Ghinita, G., Kalnis, P., & Skiadopoulos, S. (2007). MOBIHIDE: A mobilea peer-to-peer system for anonymous location-based queries. In Advances in spatial and temporal databases (pp. 221–238). Berlin: Springer.
Kalnis, P., Ghinita, G., Mouratidis, K., & Papadias, D. (2007). Preventing location-based identity inference in anonymous spatial queries. IEEE Transactions on Knowledge and Data Engineering, 19(12), 1719–1733.
Bamba, B., Liu, L., Pesti, P., & Wang, T. (2008). Supporting anonymous location queries in mobile environments with privacygrid. In Proceedings of the 17th international conference on World Wide Web (pp. 237–246). ACM.
Gedik, B., & Liu, L. (2008). Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Transactions on Mobile Computing, 7(1), 1–18.
Xu, T., & Cai, Y. (2008). Exploring historical location data for anonymity preservation in location-based services. In The 27th Conference on Computer Communications. INFOCOM 2008. IEEE.
Bettini, C., Mascetti, S., Wang, X. S., Freni, D., & Jajodia, S. (2009). Anonymity and historical-anonymity in location-based services. In Privacy in location-based applications (pp. 1–30). Berlin: Springer.
Gedik, B., & Liu, L. (2005). Location privacy in mobile systems: A personalized anonymization model. In Proceedings of the 25th IEEE international conference on distributed computing systems, 2005. ICDCS 2005 (pp. 620–629). IEEE.
Meyerowitz, J., & Choudhury, R. R. (2009). Hiding stars with fireworks: Location privacy through camouflage. In Proceedings of the 15th annual international conference on mobile computing and networking (pp. 345–356). ACM.
Shokri, R., Theodorakopoulos, G., Troncoso, C., Hubaux, J.-P., & Le Boudec, J.-Y. (2012). Protecting location privacy: Optimal strategy against localization attacks. In Proceedings of the 2012 ACM conference on computer and communications security (pp. 617–627) ACM.
Dewri, R., & Thurimella, R. (2014). Exploiting service similarity for privacy in location-based search queries. IEEE Transactions on Parallel and Distributed Systems, 25(2), 374–383.
Xiao, C., Chen, Z., Wang, X., Zhao, J., & Chen, C. (2014). DeCache: A decentralized two-level cache for mobile location privacy protection. In Sixth international conference on ubiquitous and future networks (ICUFN), 2014 (pp. 81–86). IEEE.
Damiani, M. L., Bertino, E., Silvestri, C., et al. (2010). The PROBE framework for the personalized cloaking of private locations. Transactions on Data Privacy, 3(2), 123–148.
Buchanan, W. J., Kwecka, Z., & Ekonomou, E. (2013). A privacy preserving method using privacy enhancing techniques for location based services. Mobile Networks and Applications, 18(5), 728–737. Springer.
Hearn, D. D., Baker, M. P., & Carithers, W. (2010). Computer graphics with open GL. Upper Saddle River: Prentice Hall Press.
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This paper is an extended version of the book chapter Gupta et al. (2018), published in Cyber Security, Advances in Intelligent Systems and Computing 729, https://doi.org/10.1007/978-981-10-8536-9_1.
Appendix: Few Essentials
Appendix: Few Essentials
This section describes the geometric transformations given by [22].
A reflection is a transformation that produces a mirror image of the point.
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1.
Reflection about the line \(y=0\), the x axis, is accomplished with the transformation matrix
$$\begin{aligned} M = \begin{bmatrix} 1&0&0\\ 0&-1&0\\ 0&0&1 \end{bmatrix} \end{aligned}$$keeps x value the same, but “flips” the y value of coordinate position
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2.
Reflection about the line \(x=0\), the y axis. The matrix for this transformation is
$$\begin{aligned} M = \begin{bmatrix} -1&0&0\\ 0&1&0\\ 0&0&1 \end{bmatrix} \end{aligned}$$“flips” the x value of coordinate position while keeping y the same
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3.
This transformation, referred to as a reflection relative to the coordinate origin, has the matrix representation:
$$\begin{aligned} M = \begin{bmatrix} -1&0&0\\ 0&-1&0\\ 0&0&1 \end{bmatrix} \end{aligned}$$“flip” both the x and y coordinates if a point by reflecting relative to an axis that is perpendicular to the xy plane and passes through the coordinate of origin with the transformation matrix
Final transformation is achieved through, \(P' = M \cdot P\)
A translation is a transformation applied to a point by repositioning it along a straight line path from one coordinate location to another.
We translate a 2D point by adding translation distances, \(t_x\) and \(t_y\), to the original (x, y) to move the point to a new position \((x', y')\).
i.e. \(P' = T_{(t_x, t_y)} \cdot P\).
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Gupta, R., Rao, U.P. VIC-PRO: Vicinity Protection by Concealing Location Coordinates Using Geometrical Transformations in Location Based Services. Wireless Pers Commun 107, 1041–1059 (2019). https://doi.org/10.1007/s11277-019-06316-y
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DOI: https://doi.org/10.1007/s11277-019-06316-y