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Privacy-enhanced middleware for location-based sub-community discovery in implicit social groups

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Abstract

In our connected world, recommender services have become widely known for their ability to provide expert and personalize information to participants of diverse applications. The excessive growth of social networks, a new kind of services are being embraced which are termed as “group based recommendation services”, where recommender services can be utilized to discover sub-communities within implicit social groups and provide referrals to new participants in order to join various sub-communities of other participants who share similar preferences or interests. Nevertheless, protecting participants’ privacy in recommendation services is a quite crucial aspect which might prevent participants from exchanging their own data with these services, which in turn detain the accuracy of the generated referrals. So in order to gain accurate referrals, recommendation services should have the ability to discover previously unknown sub-communities from different social groups in a way to preserve privacy of participants in each group. In this paper, we present a middleware that runs on end-users’ mobile phones to sanitize their profiles’ data when released for generating referrals, such that computation of referrals continues over the sanitized version of their profiles’ data. The proposed middleware is equipped with cryptography protocols to facilitate private discovery of sub-communities from the sanitized version of participants’ profiles in a university scenario. Location data are added to participants’ profiles to improve the awareness of surrounding sub-communities, so the offered referrals can be filtered based on adjacent locations for participant’s location. We performed a number of different experiments to test the efficiency and accuracy of our protocols. We also developed a formal model for the tradeoff between privacy level and accuracy of referrals. As supported by the experiments, the sub-communities were correctly identified with good accuracy and an acceptable privacy level.

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References

  1. Taylor D et al (2009) Privacy concern and online personalization: the moderating effects of information control and compensation. Electron Commer Res 9:203–223

    Article  Google Scholar 

  2. Yang CC et al (2003) Visualization of large category map for Internet browsing. Decis Support Syst 35:89–102

    Article  Google Scholar 

  3. Petrova K, Wang B (2011) Location-based services deployment and demand: a roadmap model. Electron Commer Res 11:5–29

    Article  Google Scholar 

  4. Xu F et al (2013) Factors affecting privacy disclosure on social network sites: an integrated model. Electron Commer Res 13:1–18

  5. Smith R, Shao J (2007) Privacy and e-commerce: a consumer-centric perspective. Electron Commer Res 7:89–116

    Article  Google Scholar 

  6. Mitnick KD, Simon WL (2002) The art of deception: controlling the human element of security. Wiley, New York

    Google Scholar 

  7. He J et al (2006) Inferring privacy information from social networks. In: Presented at the proceedings of the 4th IEEE international conference on Intelligence and Security Informatics, San Diego, CA

  8. Cockcroft SKS, Clutterbuck PJ (2001) Attitudes towards information privacy. ACIS 2001 Proceedings, 20 pp

  9. de Gemmis M et al (2009) Preference learning in recommender systems. In: Presented at the European conference on machine learning and principles and practice of knowledge discovery in databases (ECML/PKDD), Slovenia

  10. McSherry F, Mironov I (2009) Differentially private recommender systems: building privacy into the net. In: Presented at the proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, Paris, France

  11. Koutsabasis P et al (2008) A descriptive reference framework for the personalisation of e-business applications. Electron Commer Res 8:173–192

    Article  Google Scholar 

  12. Esma A (2008) Experimental demonstration of a hybrid privacy-preserving recommender system, pp 161–170

  13. Loureiro S et al (2001) Secure data collection with updates. Electron Commer Res 1:119–130

    Article  MATH  Google Scholar 

  14. Canny J (2002) Collaborative filtering with privacy via factor analysis. In: Presented at the proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, Tampere, Finland

  15. Canny J (2002) Collaborative filtering with privacy. In: Presented at the proceedings of the 2002 IEEE symposium on security and privacy

  16. Polat H, Du W (2003) Privacy-preserving collaborative filtering using randomized perturbation techniques. In: Presented at the proceedings of the third IEEE international conference on data mining

  17. Polat H, Du W (2005) SVD-based collaborative filtering with privacy. In: Presented at the proceedings of the 2005 ACM symposium on applied computing, Santa Fe, New Mexico

  18. Huang Z et al (2005) Deriving private information from randomized data. In: Presented at the proceedings of the 2005 ACM SIGMOD international conference on management of data, Baltimore, Maryland

  19. Kargupta H et al (2003) On the privacy preserving properties of random data perturbation techniques. In: Presented at the proceedings of the third IEEE international conference on data mining

  20. Miller BN et al (2004) PocketLens: toward a personal recommender system. ACM Trans Inf Syst 22:437–476

    Article  Google Scholar 

  21. Swamynathan G et al (2010) The design of a reliable reputation system. Electron Commer Res 10:239–270

    Article  MATH  Google Scholar 

  22. Elmisery A, Botvich D (2013) Multi-agent based middleware for protecting privacy in IPTV content recommender services. Multimedia Tools Appl 64(2):249–275. doi:10.1007/s11042-012-1067-3

  23. Elmisery A, Botvich D (2012) Privacy aware recommender service using multi-agent middleware—an IPTV network scenario. Informatica 36(1):21–36

  24. Elmisery A, Botvich D (2011) Enhanced middleware for collaborative privacy in IPTV recommender services. J Converg 2:10

    Google Scholar 

  25. Elmisery A, Botvich D (2011) An agent based middleware for privacy aware recommender systems in IPTV networks. In: 3rd international conference on intelligent decision technologies, University of Piraeus, Greece

  26. Elmisery A, Botvich D (2011) Agent based middleware for maintaining user privacy in IPTV recommender services. In: 3rd international ICST conference on security and privacy in mobile information and communication systems, Aalborg, Denmark

  27. Elmisery A, Botvich D (2011) Privacy aware recommender service for IPTV networks. In: 5th FTRA/IEEE international conference on multimedia and ubiquitous rngineering, Crete, Greece

  28. Elmisery AM et al (2012) Privacy aware community based recommender service for conferences attendees. In: 16th international conference on knowledge-based and intelligent information and engineering systems, San Sebastian, Spain, pp 519–531

  29. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Jacques Stern (ed) Proceedings of the 17th international conference on Theory and application of cryptographic techniques (EUROCRYPT’99), Springer, Berlin, Heidelberg, pp 223–238

  30. Li J, Pendry JB (2008) Hiding under the carpet: a new strategy for cloaking. http://arxiv.org/abs/0806.4396

  31. Ram J et al (2007) A secure multidimensional point inclusion protocol. In: Presented at the proceedings of the 9th workshop on multimedia and security, Dallas, Texas, USA

  32. Thomas T (2007) A secure multidimensional point inclusion protocol, CoRR, pp 109–120

  33. Ankerst M et al (1999) OPTICS: ordering points to identify the clustering structure. In: Presented at the proceedings of the 1999 ACM SIGMOD international conference on management of data, Philadelphia, Pennsylvania, United States

  34. Metzler D et al (2007) Similarity measures for short segments of text. In: Presented at the proceedings of the 29th European conference on IR research, Rome, Italy

  35. Cuesta-Frau D et al (2003) Clustering of electrocardiograph signals in computer-aided Holter analysis. Comput Methods Programs Biomed 72:179–196

    Article  Google Scholar 

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Acknowledgments

This work was partially financed by the Knowledge Foundation through the Internet of Things and People research profile (Malmö University, Sweden), by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2061978).

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Authors and Affiliations

  1. Department of Computer Science, Malmö University, Malmö, Sweden

    Ahmed M. Elmisery

  2. Internet of Things and People Research Center, Malmö University, Malmö, Sweden

    Ahmed M. Elmisery

  3. Department of Multimedia, Sungkyul University, Anyang, Korea

    Seungmin Rho

  4. Gaspard Monge Computer Science Laboratory, Université Paris-Est Marne-la-Vallée, Paris, France

    Dmitri Botvich

Authors
  1. Ahmed M. Elmisery
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  2. Seungmin Rho
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  3. Dmitri Botvich
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Correspondence to Seungmin Rho.

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Elmisery, A.M., Rho, S. & Botvich, D. Privacy-enhanced middleware for location-based sub-community discovery in implicit social groups. J Supercomput 72, 247–274 (2016). https://doi.org/10.1007/s11227-015-1574-x

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  • DOI: https://doi.org/10.1007/s11227-015-1574-x

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