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Privacy vs. Reward in Indoor Location-Based Services

.S. consumers reject in-store tracking said survey,” http://www.opinionlab.com/media_coverage/u-sconsumers-reject-in-store-tracking-said-survey/. [5] H. Xu, H.-H. Teo, B. Tan, and R. Agarwal, “The role of push-pull technology in privacy calculus: The case of location-based services,” J. Manage. Inf. Syst., vol. 26, no. 3, pp. 135-174, Dec. 2009. [Online]. Available: http://dx.doi.org/10.2753/MIS0742-1222260305 [6] J. Hannan, “Approximation to Bayes risk in repeated plays,” Contributions to the Theory of Games, vol. 3, pp. 97-139, 1957

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Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

apps. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services , MobiSys ’13, New York, NY, USA, 2013. ACM. [27] B. Deva, S. R. Garzon, and S. Schünemann. A context-sensitive privacy-aware framework for proactive location-based services. In 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies , Sept 2015. [28] Z. Ding, L. Guo, and X. Meng. Adaptive location update mechanism for network-constrained moving objects in changeful traffic conditions. In 2009 Tenth

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Efficient Utility Improvement for Location Privacy

, “Constructing elastic distinguishability metrics for location privacy,” PoPETs , vol. 2015, no. 2, pp. 156–170, 2015. [19] E. ElSalamouny, K. Chatzikokolakis, and C. Palamidessi, “Generalized differential privacy: Regions of priors that admit robust optimal mechanisms,” in Horizons of the Mind , vol. 8464 of LNCS , pp. 292–318, Springer Int. Publishing, 2014. [20] M. Gruteser and D. Grunwald, “Anonymous usage of location-based services through spatial and temporal cloaking,” in Proc. of MobiSys , USENIX, 2003. [21] P. Samarati and L. Sweeney

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Privacy Games: Optimal User-Centric Data Obfuscation

. Boudec. Prolonging the hide-and-seek game: Optimal trajectory privacy for location-based services. In ACM Workshop on Privacy in the Electronic Society (WPES 2014) , 2014. [54] C. Troncoso and G. Danezis. The bayesian traffic analysis of mix networks. In Proceedings of the 16th ACM conference on Computer and communications security , 2009. [55] L. Zadeh. Optimality and non-scalar-valued performance criteria. Automatic Control, IEEE Transactions on , 8, 1963.

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Tell Me Where You Are and I’ll Tell You What You Want: Using Location Data to Improve Marketing Decisions

Abstract

Location data has become more and more accessible. Smartphone applications such as location-based services collect location data on a large scale. Up to now, most approaches have relied on past data, but new developments in machine learning and artificial intelligence will soon enable more dynamic real-time use of location data. Companies that embrace these technologies will be able to create competitive advantages. Location data offers great potential to improve a variety of marketing decisions such as targeted pricing and advertising, store locations and in-store layout. Location based advertising is currently the most common application. It allows targeting all customers within a certain distance of a store. Besides advertising, location data can be used for dynamic pricing decisions. Customers close to competitor’s locations can be charged a lower price for particular products via discounts in order to reduce switching costs. Indoor tracking can help to optimize store design or the positioning of categories and brands. Granular location data about consumers’ movements hence further allows for minimizing potential offline transaction costs based on the distances to stores.

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Online data clustering algorithms in an RTLS system

References [1] S. Albers,H. Bals, Dynamic TCP acknowledgement: Penalizing long delays, SIAM J. Discrete Math. 19, 4 (2005) 938-951. →7 [2] M. Brugger, T. Christ, F. Kemeth, S. Nagy, M. Schaefer, M. M. Pietrzyk, The FMCW technology-based indoor localization system, Proc. Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Helsinki, Finnland, 2010, pp. 1-6. →6 [3] M. Brugger, F. Kemeth, Locating rate adaptation by evaluating movement specific parameters, Proc. 2010 NASA/ESA Conference on

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Location Privacy for Rank-based Geo-Query Systems

(12):2360–2372, 2013. [6] R. Dewri, W. Eltarjaman, P. Annadata, and R. Thurimella. Beyond the Thin Client Model for Location Privacy. In Proceedings of the 2013 International Conference on Privacy and Security in Mobile Systems , pages 1–8, 2013. [7] R. Dewri, I. Ray, I. Ray, and D. Whitley. Query m-Invariance: Preventing Query Disclosures in Continuous Location-Based Services. In Proceedings of the 11th International Conference on Mobile Data Management , pages 95–104, 2010. [8] J. Freudiger, R. Shokri, and J.-P. Hubaux. Evaluating the Privacy Risk of Location-Based

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An Analysis of Wi-Fi Based Indoor Positioning Accuracy

An Analysis of Wi-Fi Based Indoor Positioning Accuracy

The increasing demand for location based services inside buildings has made indoor positioning a significant research topic. This study deals with indoor positioning using the Wireless Ethernet IEEE 802.11 (Wireless Fidelity, Wi-Fi) standard that has a distinct advantage of low cost over other indoor wireless technologies. The aim of this study is to examine several aspects of location fingerprinting based indoor positioning that affect positioning accuracy. Overall, the positioning accuracy achieved in the performed experiments is 2.0 to 2.5 meters.

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Constructing elastic distinguishability metrics for location privacy

through obfuscation-based techniques. In Proc. of DAS , volume 4602 of LNCS , pages 47–60. Springer, 2007. [4] B. Bamba, L. Liu, P. Pesti, and T. Wang. Supporting anonymous location queries in mobile environments with privacygrid. In Proc. of WWW , pages 237–246. ACM, 2008. [5] N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi. Optimal geo-indistinguishable mechanisms for location privacy. In Proc. of CCS , 2014. [6] A. J. B. Brush, J. Krumm, and J. Scott. Exploring end user preferences for location obfuscation, location-based services, and

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INVESTIGATION OF PRACTICAL AND THEORETICAL ACCURACY OF WIRELESS INDOOR POSITIONING SYSTEM UBISENSE

References Coyle, L., Juan, Y., Loureiro, E., Knox, S., Dobson, S. & Nixon, P. (2007). A Proposed Approach to Evaluate the Accuracy of Tag-based Location Systems, Ubiquitous Systems Evaluation (USE 2007). Workshop at UbiComp2007, pp. 292-296, 2007 Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D. & Mc Caughey, A. (2011). An Evaluation of Indoor Location Determination Technologies, Journal of Location Based Service Volume 5, Issue 2, 2011 Gremigni, O. & Porcino, D. (2006). UWB ranging performance

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