Targeted Advertising on the Handset: Privacy and Security Challenges

  • Hamed HaddadiEmail author
  • Pan Hui
  • Tristan Henderson
  • Ian Brown
Part of the Human-Computer Interaction Series book series (HCIS)


Online advertising is currently a rich source of revenue for many Internet giants. With the ever-increasing number of smart phones, there is a fertile market for personalised and localised advertising. A key benefit of using mobile phones is to take advantage of the significant amount of information on phones – such as locations of interest to the user – in order to provide personalised advertisements. Preservation of user privacy, however, is essential for successful deployment of such a system. In this chapter we provide an overview of existing advertising systems and privacy concerns on mobile phones, in addition to a system, MobiAd, which includes protocols for scalable local advertisement download and privacy-aware click report dissemination. In the final section of this chapter we describe some of the security mechanisms used in detecting click-through fraud, and techniques that can be used to ensure that the extra privacy protections of MobiAd are not abused to defraud advertisers.


Mobile Phone Network Operator User Profile Smart Phone Content Provider 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aalto, L., Göthlin, N., Korhonen, J., Ojala, T.: Bluetooth and WAP push based location-aware mobile advertising system. In: Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services (MobiSys), pp. 49–58. ACM, New York (2004). doi: 10.1145/990064.990073Google Scholar
  2. 2.
    AdMob: Admob mobile metrics report. (2010). Accessed 23 Mar 2011
  3. 3.
    Ben Abdesslem, F., Parris, I., Henderson, T.: Mobile experience sampling: reaching the parts of Facebook other methods cannot reach. In: Proceedings of the Privacy and Usability Methods Pow-Wow (PUMP). British Computer Society. (2010). Accessed 23 Mar 2011
  4. 4.
    Burleigh, S., Hooke, A., Torgerson, L., Fall, K., Cerf, V., Durst, B., Scott, K., Weiss, H.: Delay-tolerant networking: an approach to interplanetary internet. IEEE Commun. Mag. 41(6), 128–136 (2003). doi: 10.1109/MCOM.2003.1204759CrossRefGoogle Scholar
  5. 5.
    Consolvo, S., Walker, M.: Using the experience sampling method to evaluate ubicomp applications. IEEE Pervas. Comput. 2(2), 24–31 (2003). doi: 10.1109/MPRV.2003.1203750CrossRefGoogle Scholar
  6. 6.
    Cozza, R.: Forecast: Mobile Communications Devices by Open Operating System, Worldwide, 2008–2015,
  7. 7.
    Crompton, B.: Tech Deals: Moneysupermarket Launches iPhone App. (2010). Accessed 23 Mar 2011
  8. 8.
    Dingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. In: Proceedings of the 13th USENIX Security Symposium, pp. 303–320. USENIX Association, Berkeley (2004)Google Scholar
  9. 9.
    Do, T.M., Perez, D.G.: By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM). ACM, New York (2010). doi: 10.1145/1899475.1899502Google Scholar
  10. 10.
    European Parliament: Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. OJ. L. 38(281), 31–50 (1995)Google Scholar
  11. 11.
    Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., Estrin, D.: A first look at traffic on smartphones. In: Proceedings of the 10th Annual Conference on Internet Measurement (IMC), pp. 281–287. ACM, New York (2010). doi: 10.1145/1879141.1879176Google Scholar
  12. 12.
    Fall, K.: A delay-tolerant network architecture for challenged internets. In: SIGCOMM ’03: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 27–34. ACM, New York (2003). doi: 10.1145/863955.863960Google Scholar
  13. 13.
    Freudiger, J., Vratonjic, N., Hubaux, J.P.: Towards privacy-friendly online advertising. In: Proceedings of W2SP 2009: Web 2.0 Security and Privacy. (2009). Accessed 23 Mar 2011
  14. 14.
    Greenstein, B., McCoy, D., Pang, J., Kohno, T., Seshan, S., Wetherall, D.: Improving wireless privacy with an identifier-free link layer protocol. In: MobiSys ’08: Proceeding of the 6th International Conference on Mobile Systems, Applications, and Services, pp. 40–53. ACM, New York (2008). doi: 10.1145/1378600.1378607Google Scholar
  15. 15.
    Google Advertising Revenue: Accessed 23 Mar 2011
  16. 16.
    Guha, S., Reznichenko, A., Tang, K., Haddadi, H., Francis, P.: Serving ads from localhost for performance, privacy, and profit. In: HotNets-VIII: Proceedings of the Eighth ACM Workshop on Hot Topics in Networks. (2009). Accessed 23 Mar 2011Google Scholar
  17. 17.
    Guha, S., Cheng, B., Francis, P.: Challenges in measuring online advertising systems. In: Proceedings of the 10th Annual Conference on Internet Measurement (IMC), pp. 81–87. ACM, New York (2010). doi: 10.1145/1879141.1879152Google Scholar
  18. 18.
    Haddadi, H.: Fighting online click-fraud using bluff ads. ACM SIGCOMM Comput. Commun. Rev. 40(2), 21–25 (2010). doi: 10.1145/1764873.1764877CrossRefGoogle Scholar
  19. 19.
    Henderson, T., Ben Abdesslem, F.: Scaling measurement experiments to planet-scale: ethical, regulatory and cultural considerations. In: HotPlanet ’09: Proceedings of the 1st ACM International Workshop on Hot Topics of Planet-Scale Mobility Measurements, pp. 1–5. ACM, New York (2009). doi: 10.1145/1651428.1651436Google Scholar
  20. 20.
    iAd service: Accessed 23 Mar 2011
  21. 21.
    Hui, P., Crowcroft, J., Yoneki, E.: BUBBLE rap: social-based forwarding in delay tolerant networks. In: MobiHoc ’08: Proceedings of the 9th ACM International Symposium on Mobile ad Hoc Networking and Computing, pp. 241–250. ACM, New York (2008). doi: 10.1145/1374618.1374652Google Scholar
  22. 22.
    Immorlica, N., Jain, K., Mahdian, M., Talwar, K.: Click fraud resistant methods for learning click-through rates. In: Deng, X., Ye, Y. (eds.) Internet and Network Economics. Lecture Notes in Computer Science, vol. 3828, pp. 34–45. Springer, Berlin/Heidelberg (2005). doi: 10.1007/11600930_5Google Scholar
  23. 23.
    Juels, A., Stamm, S., Jakobsson, M.: Combating click fraud via premium clicks. In: SS’07: Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium, pp. 1–10. USENIX Association, Berkeley (2007)Google Scholar
  24. 24.
    Komulainen, H., Ristola, A., Still, J.: Mobile advertising in the eyes of retailers and consumers – empirical evidence from a real-life experiment. In: Proceedings of the International Conference on Mobile Business, p. 37. IEEE Computer Society, Washington (2006). doi: 10.1109/ICMB.2006.31Google Scholar
  25. 25.
    Larson, R., Csikszentmihalyi, M.: The experience sampling method. New Dir. Methodol. Soc. Behav. Sci. 15, 41–56 (1983)Google Scholar
  26. 26.
    Lu, X., Hui, P., Towsley, D., Pu, J., Xiong, Z.: Anti-localization anonymous routing for Delay Tolerant network. Comput. Netw. 54(11), 1899–1910 (2010). doi: 10.1016/j.comnet.2010.03.002zbMATHCrossRefGoogle Scholar
  27. 27.
    Mancini, C., Thomas, K., Rogers, Y., Price, B.A., Jedrzejczyk, L., Bandara, A.K., Joinson, A.N., Nuseibeh, B.: From spaces to places: emerging contexts in mobile privacy. In: Ubicomp ’09: Proceedings of the 11th International Conference on Ubiquitous Computing, pp. 1–10. ACM, New York (2009). doi: 10.1145/1620545.1620547Google Scholar
  28. 28.
    MBMS: Multimedia Broadcast/Multicast Service (MBMS); Stage 1, 3GPP Specification. (2010). Accessed 23 Mar 2011
  29. 29.
    Merisavo, M., Vesanen, J., Arponen, A., Kajalo, S., Raulas, M.: The effectiveness of targeted mobile advertising in selling mobile services: an empirical study. Int. J. Mob. Commun. 4(2), 119–127. (2006). Accessed 23 Mar 2011Google Scholar
  30. 30.
    Milgram, S.: The small-world problem. Psychol. Today 1(1), 61–67 (1967)MathSciNetGoogle Scholar
  31. 31.
    Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. Soc. Sci. Res. Network Working Paper Series. (2009). Accessed 23 Mar 2011
  32. 32.
    Oliver, S. iPod touch users spend more time using apps than those with iPhones. (2010). Accessed 23 Mar 2011
  33. 33.
    Parris, I., Henderson, T.: Privacy-enhanced social-network routing. Comput. Commun. In Press, Corrected Proof, Available online 19 November 2010, ISSN 0140-3664, doi: 10.1016/j.comcom.2010.11.003. ( zbMATHGoogle Scholar
  34. 34.
    Ranganathan, A., Campbell, R.H.: Advertising in a pervasive computing environment. In: WMC ’02: Proceedings of the 2nd International Workshop on Mobile Commerce, pp. 10–14. ACM, New York (2002). doi: 10.1145/570705.570708Google Scholar
  35. 35.
    Shepard, C., Tossel, C., Rahmati, A., Zhong, L., Kortum, P.: Livelab: measuring wireless networks and smartphone users in the field. In: Proceedings of The 3rd Workshop on Hot Topics in Measurement and Modeling of Computer Systems (HotMetrics). (2010). Accessed 23 Mar 2011
  36. 36.
    Shye, A., Scholbrock, B., Memik, G.: Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO 42). ACM, New York, USA, pp. 168–178. doi=10.1145/1669112.1669135, (2009)
  37. 37.
    Song, C., Qu, Z., Blumm, N., Barabasi, A.L.: Limits of predictability in Human mobility. Science 327(5968), 1018–1021 (2010). doi: 10.1126/science.1177170MathSciNetCrossRefGoogle Scholar
  38. 38.
    Spyropoulos, T., Psounis, K., Raghavendra, C.: Efficient routing in intermittently connected mobile networks: the single-copy case. IEEE/ACM Trans. Netw. 16, 1 (February 2008), pp. 63–76. doi=10.1109/TNET.2007.897962, (2008)Google Scholar
  39. 39.
    Torproject Website: Accessed 23 Mar 2011
  40. 40.
    Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., Barocas, S.: Adnostic: privacy preserving targeted advertising. In: Proceedings of the 17th Annual Network and Distributed System Symposium. Internet Society, San Diego (2010)Google Scholar
  41. 41.
    Turow, J., Hennessy, M.: Internet privacy and institutional trust. New Media Soc. 9(2), 300–318 (2010). doi: 10.1177/1461444807072219CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Hamed Haddadi
    • 1
    Email author
  • Pan Hui
    • 2
  • Tristan Henderson
    • 3
  • Ian Brown
    • 4
  1. 1.Queen Mary, University of LondonLondonUK
  2. 2.Deutsche Telekom LaboratoriesDarmstadtGermany
  3. 3.University of St AndrewsSt AndrewsUK
  4. 4.Oxford Internet InstituteOxfordUK

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