Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Proceedings on Privacy Enhancing Technologies

4 Issues per year

Open Access
Online
ISSN
2299-0984
See all formats and pricing
In This Section

Privacy vs. Reward in Indoor Location-Based Services

Kassem Fawaz
  • Corresponding author
  • University of Michigan
  • Email:
/ Kyu-Han Kim
  • Hewlett Packard Labs
  • Email:
/ Kang G. Shin
  • University of Michigan
  • Email:
Published Online: 2016-07-14 | DOI: https://doi.org/10.1515/popets-2016-0031

Abstract

With the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

Keywords: Location Privacy; Indoor localization; Differential Privacy

References

  • [1] S. Sen, J. Lee, K.-H. Kim, and P. Congdon, “Avoiding multipath to revive inbuilding wifi localization,” in Proceeding of MobiSys ’13, 2013, pp. 249-262. [Online]. Available: http://doi.acm.org/10.1145/2462456.2464463CrossrefGoogle Scholar

  • [2] A. Martin, “Nordstrom no longer tracking customer phones,” http://cbsloc.al/1JIYNlR, May 2013.Google Scholar

  • [3] Future of Privacy Forum, “Mobile Location Analytics Code of Conduct,” http://www.futureofprivacy.org/wpcontent/uploads/10.22.13-FINAL-MLA-Code.pdf.Google Scholar

  • [4] L. Privat, “U.S. consumers reject in-store tracking said survey,” http://www.opinionlab.com/media_coverage/u-sconsumers-reject-in-store-tracking-said-survey/.Google Scholar

  • [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/MIS0742-1222260305CrossrefGoogle Scholar

  • [6] J. Hannan, “Approximation to Bayes risk in repeated plays,” Contributions to the Theory of Games, vol. 3, pp. 97-139, 1957.Google Scholar

  • [7] D. P. D. Farias and N. Megiddo, “Combining expert advice in reactive environments,” J. ACM, vol. 53, no. 5, pp. 762-799, Sep. 2006. [Online]. Available: http://doi.acm.org/10.1145/1183907.1183911CrossrefGoogle Scholar

  • [8] Apple Support, “iOS: Understanding iBeacon,” https://support.apple.com/en-gb/HT202880, Feb. 2015.Google Scholar

  • [9] R. Rodrigues, D. Barnard-Wills, D. Wright, P. De Hert, V. Papakonstantinou, L. Beslay, E. JRC-IPSC, N. Dubois, and E. JUST, “EU privacy seals project,” Publications Office of the European Union, 2013.Google Scholar

  • [10] P. Higgins and L. Tien, “Mobile tracking code of conduct falls short of protecting consumers,” https://www.eff.org/deeplinks/2013/10/mobile-tracking-code-conduct-falls\-short-protecting-consumers, October 2013.Google Scholar

  • [11] L. Demir, M. Cunche, and C. Lauradoux, “Analysing the privacy policies of Wi-Fi trackers,” in Workshop on Physical Analytics, Bretton Woods, USA, Jun. 2014. [Online]. Available: http://hal.inria.fr/hal-00983363Google Scholar

  • [12] , “Apple - privacy built in,” https://www.apple.com/privacy/privacy-built-in/.Google Scholar

  • [13] M. Gruteser and D. Grunwald, “Enhancing location privacy in wireless LAN through disposable interface identifiers: A quantitative analysis,” Mob. Netw. Appl., vol. 10, no. 3, pp. 315-325, Jun. 2005.Google Scholar

  • [14] T. Jiang, H. J. Wang, and Y.-C. Hu, “Preserving location privacy in wireless LANs,” in Proceedings of MobiSys ’07, 2007, pp. 246-257. [Online]. Available: http://doi.acm.org/10.1145/1247660.1247689CrossrefGoogle Scholar

  • [15] M. Li, K. Sampigethaya, L. Huang, and R. Poovendran, “Swing & swap: User-centric approaches towards maximizing location privacy,” in Proceedings of WPES ’06, 2006, pp. 19-28. [Online]. Available: http://doi.acm.org/10.1145/1179601.1179605CrossrefGoogle Scholar

  • [16] C. Riederer, V. Erramilli, A. Chaintreau, B. Krishnamurthy, and P. Rodriguez, “For sale : Your data: By : You,” in Proceedings of HotNets-X. New York, NY, USA: ACM, 2011, pp. 13:1-13:6. [Online]. Available: http://doi.acm.org/10.1145/2070562.2070575CrossrefGoogle Scholar

  • [17] A. Ghosh and A. Roth, “Selling privacy at auction,” in Proceedings of EC ’11, 2011, pp. 199-208. [Online]. Available: http://doi.acm.org/10.1145/1993574.1993605CrossrefGoogle Scholar

  • [18] R. Shokri, “Privacy games: Optimal user-centric data obfuscation,” Proceedings on Privacy Enhancing Technologies, vol. 2015, no. 2, pp. 1-17, 2015.Google Scholar

  • [19] P. Kumaraguru and L. F. Cranor, “Privacy Indexes: A Survey of Westin’s Studies,” Carnegie Mellon University, Institute for Software Research International, Tech. Rep., 12 2005.Google Scholar

  • [20] V. Rastogi and S. Nath, “Differentially private aggregation of distributed time-series with transformation and encryption,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD ’10. New York, NY, USA: ACM, 2010, pp. 735-746. [Online]. Available: http://doi.acm.org/10.1145/1807167.1807247CrossrefGoogle Scholar

  • [21] O. Abul, F. Bonchi, and M. Nanni, “Never walk alone: Uncertainty for anonymity in moving objects databases,” in Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ser. ICDE ’08. Washington, DC, USA: IEEE Computer Society, 2008, pp. 376-385. [Online]. Available: http://dx.doi.org/10.1109/ICDE.2008.4497446CrossrefGoogle Scholar

  • [22] M. Terrovitis and N. Mamoulis, “Privacy preservation in the publication of trajectories,” in Proceedings of the The Ninth International Conference on Mobile Data Management, ser. MDM ’08. Washington, DC, USA: IEEE Computer Society, 2008, pp. 65-72. [Online]. Available: http://dx.doi.org/10.1109/MDM.2008.29CrossrefGoogle Scholar

  • [23] R. Chen, G. Acs, and C. Castelluccia, “Differentially private sequential data publication via variable-length n-grams,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: ACM, 2012, pp. 638-649. [Online]. Available: http://doi.acm.org/10.1145/2382196.2382263CrossrefGoogle Scholar

  • [24] M. E. Andrés, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geo-indistinguishability: Differential privacy for location-based systems,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, ser. CCS ’13. New York, NY, USA: ACM, 2013, pp. 901-914. [Online]. Available: http://doi.acm.org/10.1145/2508859.2516735CrossrefGoogle Scholar

  • [25] K. Chatzikokolakis, C. Palamidessi, and M. Stronati, “A predictive differentially-private mechanism for mobility traces,” in Privacy Enhancing Technologies. Springer, 2014, pp. 21-41.Google Scholar

  • [26] G. Barthe, B. Köpf, F. Olmedo, and S. Zanella Béguelin, “Probabilistic relational reasoning for differential privacy,” in Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, ser. POPL ’12. New York, NY, USA: ACM, 2012, pp. 97-110. [Online]. Available: http://doi.acm.org/10.1145/2103656.2103670CrossrefGoogle Scholar

  • [27] J. Reed and B. C. Pierce, “Distance makes the types grow stronger: A calculus for differential privacy,” in Proceedings of the 15th ACM SIGPLAN International Conference on Functional Programming, ser. ICFP ’10. New York, NY, USA: ACM, 2010, pp. 157-168. [Online]. Available: http://doi.acm.org/10.1145/1863543.1863568CrossrefGoogle Scholar

  • [28] K. Chatzikokolakis, M. Andrés, N. Bordenabe, and C. Palamidessi, “Broadening the scope of differential privacy using metrics,” in Privacy Enhancing Technologies, ser. Lecture Notes in Computer Science, E. De Cristofaro and M. Wright, Eds. Springer Berlin Heidelberg, 2013, vol. 7981, pp. 82-102. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-39077-7_5CrossrefGoogle Scholar

  • [29] C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Proceedings of the Third Conference on Theory of Cryptography, ser. TCC’06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 265-284. [Online]. Available: http://dx.doi.org/10.1007/11681878_14CrossrefGoogle Scholar

  • [30] G. Miklau and D. Suciu, “A formal analysis of information disclosure in data exchange,” in Proceedings of SIGMOD ’04, 2004, pp. 575-586. [Online]. Available: http://doi.acm.org/10.1145/1007568.1007633CrossrefGoogle Scholar

  • [31] C. Goodwin, “A conceptualization of motives to seek privacy for nondeviant consumption,” Journal of Consumer Psychology, vol. 1, no. 3, pp. 261 - 284, 1992. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1057740808800393Google Scholar

  • [32] B. Huberman, E. Adar, and L. Fine, “Valuating privacy,” Security Privacy, IEEE, vol. 3, no. 5, pp. 22-25.Google Scholar

  • [33] L. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338 - 353, 1965. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S001999586590241XGoogle Scholar

  • [34] J. Demko-Rihter and I. t. Halle, “Revival of high street retailing - the added value of shopping apps,” The AMFITEATRU ECONOMIC journal, vol. 17, no. 39, 2015. [Online]. Available: http://EconPapers.repec.org/RePEc:aes:amfeco:v:39:y:2015:i:17:p:632Google Scholar

  • [35] H. Jang, I. Ko, and J. Kim, “The effect of group-buy social commerce and coupon on satisfaction and continuance intention - focusing on the expectation confirmation model (ecm),” in System Sciences (HICSS), 2013 46th Hawaii International Conference on, Jan 2013, pp. 2938-2948.Google Scholar

  • [36] T. Kowatsch and W. Maass, “In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences,” Computers in Human Behavior, vol. 26, no. 4, pp. 697 - 704, 2010, emerging and Scripted Roles in Computer-supported Collaborative Learning. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0747563210000087Google Scholar

  • [37] I. M. Dinner, H. J. Van Heerde, and S. Neslin, “Creating Customer Engagement Via Mobile Apps:How App Usage Drives Purchase Behavior,” Social Science Research Network Working Paper Series, Oct. [Online]. Available: http://ssrn.com/abstract=2669817Google Scholar

  • [38] J.-Y. M. Kang, J. M. Mun, and K. K. Johnson, “In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps,” Computers in Human Behavior, vol. 46, pp. 210 - 217, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0747563215000242Google Scholar

  • [39] Sales Force, “2014 Mobile Behavior Report,” https://www.exacttarget.com/sites/exacttarget/files/deliverables/etmc-2014mobilebehaviorreport.pdf, Feb. 2014.Google Scholar

  • [40] K. L. S. Ohri, “The New Digital Divide: Retailers, shoppers, and the digital influence factor,” http://www2.deloitte.com/content/dam/Deloitte/us/Documents/consumer-business/us-rd-thenewdigitaldivide-041814.pdf, 2014.Google Scholar

  • [41] D. Kosir, “Mobile apps vs. mobile web: What retailers need to know,” http://clearbridgemobile.com/mobile-apps-vs-mobileweb-what-retailers-need-to-know/, Aug. 2015.Google Scholar

  • [42] R. Libfrand, “Retail Mobile App Users Visit Brick-and-Mortars More Often,” http://blog.compariscope.com/retail-mobileapps-12x-the-number-of-in-store-visits, Jan. 2016.Google Scholar

  • [43] C. Boyle, “Mobile Messaging Trends-Tapping into SMS, Mobile Email and Push,” http://www.slideshare.net/eMarketerInc/emarketer-webinar-mobile-messaging-trendstapping-into-smsmobile-email-and-push-25068768, Aug. 2013.Google Scholar

  • [44] C. Shepard, A. Rahmati, C. Tossell, L. Zhong, and P. Kortum, “Livelab: Measuring wireless networks and smartphone users in the field,” SIGMETRICS Perform. Eval. Rev., vol. 38, no. 3, pp. 15-20, Jan. 2011. [Online]. Available: http://doi.acm.org/10.1145/1925019.1925023CrossrefGoogle Scholar

  • [45] A. Nandugudi, A. Maiti, T. Ki, F. Bulut, M. Demirbas, T. Kosar, C. Qiao, S. Y. Ko, and G. Challen, “Phonelab: A large programmable smartphone testbed,” in Proceedings of SENSEMINE’13, 2013, pp. 4:1-4:6. [Online]. Available: http://doi.acm.org/10.1145/2536714.2536718CrossrefGoogle Scholar

  • [46] P. Yin, P. Luo, W.-C. Lee, and M. Wang, “App recommendation: A contest between satisfaction and temptation,” in Proceedings of WSDM ’13. New York, NY, USA: ACM, 2013, pp. 395-404. [Online]. Available: http://doi.acm.org/10.1145/2433396.2433446CrossrefGoogle Scholar

  • [47] aestetix and C. Petro, “CRAWDAD data set hope/amd (v. 2008-08-07),” Downloaded from http://crawdad.org/hope/amd/, Aug. 2008.Google Scholar

  • [48] T. Goodspeed and N. Filardo, “CRAWDAD data set hope/nh_amd (v. 2010-07-18),” Downloaded from http://crawdad.org/hope/nh_amd/, Jul. 2010.Google Scholar

  • [49] I. Rhee, M. Shin, S. Hong, K. Lee, S. Kim, and S. Chong, “CRAWDAD data set ncsu/mobilitymodels (v. 2009-07-23),” Downloaded from http://crawdad.org/ncsu/mobilitymodels/, Jul. 2009.Google Scholar

  • [50] J. Little and B. O’Brien, “A technical review of cisco’s wi-fi-based location analytics,” http://www.cisco.com/c/en/us/products/collateral/wireless/mobility-services-engine/white_paper_c11-728970.pdf, July 2013.Google Scholar

  • [51] Derek Top, “Indoor Location Firm Nomi Faces Layoffs; Privacy Concerns To Blame?” http://t.co/e7Gp7mU1Sz, Aug. 2014.Google Scholar

About the article

Received: 2016-02-29

Revised: 2016-06-02

Accepted: 2016-06-02

Published Online: 2016-07-14

Published in Print: 2016-10-01


Citation Information: Proceedings on Privacy Enhancing Technologies, ISSN (Online) 2299-0984, DOI: https://doi.org/10.1515/popets-2016-0031.

Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in