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TPP: Tradeoff Between Personalization and Privacy

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Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 (IMCOM 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 935))

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Abstract

Modern technology relies on personalization due to its appealing services. It suggests the most relevant information to the users. Beside it several benefits, there may be some privacy leakage due to the personalization. As it analyzes and collects the user behavior’s data, and generates a personalized decision. In this paper, we have considered the personalization aspect of recommendation, crowdsensing, and healthcare domains. We have identified the state-of-the-art research, specifically emphasizing on the personalization and privacy aspect. Also, we have conducted a survey, in order to identify the literacy of personalization and privacy. Moreover, we have discussed the attacks that exploit the vulnerability of personalization.

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References

  1. Agu, E., Claypool, M.: Cypress: a cyber-physical recommender system to discover smartphone exergame enjoyment. In: Proceedings of the ACM Workshop on Engendering Health with Recommender Systems (2016)

    Google Scholar 

  2. Dharia, S., Jain, V., Patel, J., Vora, J., Chawla, S., Eirinaki, M.: PRO-Fit: a personalized fitness assistant framework. In: SEKE, pp. 386–389 (2016)

    Google Scholar 

  3. Weinberger, M., Bouhnik, D.: Place determinants for the personalization-privacy tradeoff among students. Issues Informing Sci. Inf. Technol. 15, 079–095 (2018)

    Article  Google Scholar 

  4. Katragadda, B., Sharife, S.M.: Supporting privacy protection in personalized web search. J. Sci. Technol. (JST) 2, 17–21 (2017)

    Google Scholar 

  5. Divekar, M.J., Patil, D.R.: Enabling personalized search over encrypted outsourced data with efficiency improvement. Int. J. 3 (2018)

    Google Scholar 

  6. Xing, X., Meng, W., Doozan, D., Snoeren, A.C., Feamster, N., Lee, W.: Take this personally: pollution attacks on personalized services. In: USENIX Security Symposium, pp. 671–686 (2013)

    Google Scholar 

  7. Miao, C., Li, Q., Xiao, H., Jiang, W., Huai, M., Su, L.: Towards data poisoning attacks in crowd sensing systems. In: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 111–120. ACM (2018)

    Google Scholar 

  8. Bansal, G.: Got Phished! Role of Top Management Support in Creating Phishing Safe Organizations (2018)

    Google Scholar 

  9. Sedhain, S., Sanner, S., Braziunas, D., Xie, L., Christensen, J.: Social collaborative filtering for cold-start recommendations. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 345–348. ACM (2014)

    Google Scholar 

  10. Sedhain, S., Sanner, S., Xie, L., Kidd, R., Tran, K.-N., Christen, P.: Social affinity filtering: recommendation through fine-grained analysis of user interactions and activities. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 51–62. ACM (2013)

    Google Scholar 

  11. Gardner, Z., Leibovici, D., Basiri, A., Foody, G.: Trading-off location accuracy and service quality: privacy concerns and user profiles. In: 2017 International Conference on Localization and GNSS (ICL-GNSS), pp. 1–5. IEEE (2017)

    Google Scholar 

  12. Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 194–201. ACM (2010)

    Google Scholar 

  13. Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 201–210. ACM (2009)

    Google Scholar 

  14. Groh, G., Ehmig, C.: Recommendations in taste related domains: collaborative filtering vs. social filtering. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work. pp. 127–136. ACM (2007)

    Google Scholar 

  15. Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Trans. Knowl. Data Eng. 22, 179–192 (2010)

    Article  Google Scholar 

  16. Bhamidipati, S., Fawaz, N., Kveton, B., Zhang, A.: PriView: personalized media consumption meets privacy against inference attacks. IEEE Softw. 32, 53–59 (2015)

    Article  Google Scholar 

  17. Hannon, J., McCarthy, K., Smyth, B.: Finding useful users on twitter: twittomender the followee recommender. In: European Conference on Information Retrieval, pp. 784–787. Springer (2011)

    Google Scholar 

  18. Wang, X., Liu, H., Fan, W.: Connecting users with similar interests via tag network inference. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1019–1024. ACM (2011)

    Google Scholar 

  19. Jüschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems: AI Commun. 21, 231–247 (2008)

    Google Scholar 

  20. Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: Proceedings of the 15th International Conference on World Wide Web, pp. 953–954. ACM (2006)

    Google Scholar 

  21. Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 61–68. ACM (2009)

    Google Scholar 

  22. Wu, L., Yang, L., Yu, N., Hua, X.-S.: Learning to tag. In: Proceedings of the 18th International Conference on World Wide Web, pp. 361–370. ACM (2009)

    Google Scholar 

  23. Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)

    MathSciNet  MATH  Google Scholar 

  24. Personalization Trends. https://www.emarketer.com/Report/Personalization-Retail-Latest-Trends-Challenges/2002008

  25. Jeong, Y., Kim, Y.: Privacy concerns on social networking sites: Interplay among posting types, content, and audiences. Comput. Hum. Behav. 69, 302–310 (2017)

    Article  Google Scholar 

  26. Farkas, K., Nagy, A.Z., Tomás, T., Szabó, R.: Participatory sensing based real-time public transport information service. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 141–144. IEEE (2014)

    Google Scholar 

  27. Rana, R.K., Chou, C.T., Kanhere, S.S., Bulusu, N., Hu, W.: Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 105–116. ACM (2010)

    Google Scholar 

  28. Chen, C., Huang, Y., Liu, Y., Liu, C., Meng, L., Sun, Y., Bian, K., Huang, X., Jiao, B.: Interactive crowdsourcing to spontaneous reporting of adverse drug reactions. In: 2014 IEEE International Conference on Communications (ICC), pp. 4275–4280. IEEE (2014)

    Google Scholar 

  29. Hu, S., Su, L., Liu, H., Wang, H., Abdelzaher, T.F.: Smartroad: smartphone-based crowd sensing for traffic regulator detection and identification. ACM Trans. Sens. Netw. (TOSN) 11(4), 55 (2015)

    Google Scholar 

  30. Wang, C., Liu, H., Wright, K.L., Krishnamachari, B., Annavaram, M.: A privacy mechanism for mobile-based urban traffic monitoring. Pervasive Mobile Comput. 20, 1–12 (2015)

    Article  Google Scholar 

  31. Afzal, M., Ali, S.I., Ali, R., Hussain, M., Ali, T., Khan, W.A., Amin, M.B., Kang, B.H., Lee, S.: Personalization of wellness recommendations using contextual interpretation. Expert Syst. Appl. 96, 506–521 (2018)

    Article  Google Scholar 

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Acknowledgments

This research was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00655). This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion) and NRF- 2016K1A3A7A03951968.

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Authors and Affiliations

  1. Kyung Hee University, Global Campus, Yongin-si, Republic of Korea

    Ubaid Ur Rehman & Sungyoung Lee

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  1. Ubaid Ur Rehman
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  2. Sungyoung Lee
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Correspondence to Sungyoung Lee .

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Editors and Affiliations

  1. Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Korea (Republic of)

    Sukhan Lee

  2. Software Engineering, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

    Roslan Ismail

  3. College of Software, Sungkyunkwan University, Suwon, Korea (Republic of)

    Hyunseung Choo

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Rehman, U.U., Lee, S. (2019). TPP: Tradeoff Between Personalization and Privacy. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_54

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