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Privacy Preserving Personalization in Complex Ecosystems

  • Anders AndersenEmail author
  • Randi Karlsen
Chapter

Abstract

Personalization can be used to improve the quality of a service for a user. From the providers’ perspective personalization can be used to better target its users. Personalization is achieved by creating and maintaining a user profile that describes the user, her interest and her current context. A major concern with creating, maintaining and using user profiles is user privacy and trust. In this chapter we will discuss the process of creating and maintaining user profiles with a privacy preserving focus.

References

  1. 1.
    M. Gao, K. Liu and Z. Wu, “Personalisation in web computing and informatics: Theories, techniques, applications, and future research,” Information Systems Frontiers, vol. 12, no. 5, pp. 607–629, 2010.CrossRefGoogle Scholar
  2. 2.
    M. Wiesner and D. Pfeifer, “Health recommender systems: concepts, requirements, technical basics and challenges,” International journal of environmental research and public health, vol. 11, no. 3, pp. 2580–2607, 2014.CrossRefGoogle Scholar
  3. 3.
    B. Krulwich, “Lifestyle finder: Intelligent user profiling using large-scale demographic data,” AI magazine, vol. 18, no. 2, p. 37, 1997.Google Scholar
  4. 4.
    S.-H. Min and I. Han, “Detection of the customer time-variant pattern for improving recommender systems,” Expert Systems with Applications, vol. 28, no. 2, pp. 189–199, 2005.CrossRefGoogle Scholar
  5. 5.
    A. S. Das, M. Datar, A. Garg and S. Rajaram, “Google news personalization: scalable online collaborative filtering,” in Proceedings of the 16th international conference on World Wide Web, Banff, AB, Canada, 2007.CrossRefGoogle Scholar
  6. 6.
    I. Guy, N. Zwerdling, I. Ronen, D. Carmel and E. Uziel, “Social media recommendation based on people and tags,” in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, Geneva, Switzerland, 2010.CrossRefGoogle Scholar
  7. 7.
    R. W. White, P. Bailey and L. Chen, “Predicting user interests from contextual information,” in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, Boston, MA, USA, 2009.CrossRefGoogle Scholar
  8. 8.
    R. W. White and J. Huang, “Assessing the scenic route: measuring the value of search trails in web logs,” in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, Geneva, Switzerland, 2010.CrossRefGoogle Scholar
  9. 9.
    M. Harvey, F. Crestani and M. J. Carman, “Building user profiles from topic models for personalised search,” in Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, San Francisco, CA, USA, 2013.CrossRefGoogle Scholar
  10. 10.
    P. N. Bennett, R. W. White, W. Chu, S. T. Dumais, P. Bailey, F. Borisyuk and X. Cui, “Modeling the Impact of Short- and Long-term Behavior on Search Personalization,” in Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, Oregon, USA, 2012.CrossRefGoogle Scholar
  11. 11.
    M. Balabanovic and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997.CrossRefGoogle Scholar
  12. 12.
    M. Gao, K. Liu and Z. Wu, “Personalisation in web computing and informatics: Theories, techniques, applications, and future research,” Information Systems Frontiers, vol. 12, no. 5, pp. 607–629, 2010.CrossRefGoogle Scholar
  13. 13.
    M. R. Ghorab, D. Zhou, A. O’Connor and V. Wade, “Personalised information retrieval: survey and classification,” User Modeling and User-Adapted Interaction, vol. 23, no. 4, pp. 381–443, 2013.CrossRefGoogle Scholar
  14. 14.
    S. Gauch, M. Speretta, A. Chandramouli and A. Micarelli, “User profiles for personalized information access,” in The adaptive web, P. Brusilovsky, A. Kobsa and W. Nejdl, Eds., Berlin Heidelberg NewYork, Springer, 2007, pp. 54–89.CrossRefGoogle Scholar
  15. 15.
    A. Andersen and R. Karlsen, “User profiling through NFC interactions: Mining NFC-based user information from mobile devices and back-end systems,” in Proceedings of the 14th International Symposium on Mobility Management and Wireless Access, Malta, 2016.CrossRefGoogle Scholar
  16. 16.
    S. Panjwani, N. Shrivastava, S. Shukla and S. Jaiswal, “Understanding the privacy-personalization dilemma for web search: a user perspective,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 2013.CrossRefGoogle Scholar
  17. 17.
    M. Alaggan, S. Gambs and A.-M. Kermarrec, “Heterogeneous differential privacy,” arXiv preprint arXiv:1504.06998, 2015.Google Scholar
  18. 18.
    A. Andersen, T. Hardersen and N. Schirmer, “Privacy for Cloud Storage,” in ISSE 2014 Securing Electronic Business Processes: Highlights of the Information Security Solutions Europe 2014 Conference, Springer, 2014.Google Scholar
  19. 19.
    E. Toch, Y. Wang and L. F. Cranor, “Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems,” User Modeling and User-Adapted Interaction, vol. 22, no. 1–2, pp. 203–220, 2012.CrossRefGoogle Scholar
  20. 20.
    A. Friedman, B. P. Knijnenburg, K. Vanhecke, L. Martens and S. Berkovsky, “Privacy aspects of recommender systems,” in Recommender Systems Handbook, Springer, 2015, pp. 649–688.CrossRefGoogle Scholar
  21. 21.
    L. Cassel and U. Wolz, “Client Side Personalization,” in Proceedings of the joint DELOS-NSF workshop on personalization and recommender systems in digital libraries, Dublin, 2001.Google Scholar
  22. 22.
    M. Asif and J. Krogstie, “Mobile client-side personalization,” in Proceedings of the 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS), 2013.CrossRefGoogle Scholar
  23. 23.
    A. Munch-Ellingsen, A. Andersen, S. Akselsen and R. Karlsen, “Customer managed security domain on mobile network operators’ SIM cards: Opportunities to enable new business models,” in Marktplätze im Umbruch: Digitale Strategien und das Zusammenwachsen von Shop, Online-Business sowie Services im Mobilen Internet, Springer, 2015.Google Scholar
  24. 24.
    C. Dwork, “Differential privacy: A survey of results,” in Proceedings of the International Conference on Theory and Applications of Models of Computation, 2008.zbMATHGoogle Scholar
  25. 25.
    A. Andersen, K. Y. Yigzaw and R. Karlsen, “Privacy preserving health data processing,” in Proceedings of the 16th International Conference on e-Health Networking, Applications and Services (Healthcom), 2014.CrossRefGoogle Scholar
  26. 26.
    C. Gentry, A fully homomorphic encryption scheme, Stanford University, 2009.zbMATHGoogle Scholar

Further Reading

  1. 27.
    M. K. L. Z. W. Gao, “Personalisation in web computing and informatics: Theories, techniques, applications, and future research,” Information Systems Frontiers, vol. 12, no. 5, pp. 607–629, 2010.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.UiT The Arctic University of NorwayTromsøNorway

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