Abstract
For recommending the best result to the user as per his requirement, User Profiling plays an important role. In user profiling, the profiles are created from the past data of same user. Maintaining the security and privacy of this data becomes a big challenge for researchers. Here, we are proposing the algorithm for privacy and security purpose of different profiles, with the integration of Information Dispersal Algorithm. The use of vast data of profiles by the user from any location at any time would be achieved by the use of the private cloud. As the profiles of different devices are maintained on the central cloud server, the recommendation for user for particular device can be executed easily.
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References
Atote B, Zahoor S, Dangra B, Bedekar M (2016) Personalization in user profiling privacy security. In: IEEE sponsored an international conference on internet of things and applications (IOTA 2016) at MIT, Pune
Toch E, Wang Y, Cranor LF (2012) Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. J User Model User-Adap Inter 22(1–2):203–220
Hasan O, Habegger B, Brunie L, Bennani N, Damiani E (2013) A discussion of privacy challenges in user profiling with big data techniques: the EEXCESS use case. In: IEEE 2nd international congress on big data, Santa Clara Marriott, CA, USA
Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets (how to break anonymity of the Netix Prize Dataset). In: IEEE symposium on security and privacy. Oakland, pp 111–125
Hull G, Lipford HR, Latulipe C (2011) Contextual gaps: privacy issues on facebook. Ethics Inf Technol 13(4):289–302
Chen D, Zhao H (2012) Data security and privacy protection issues in cloud computing, In: International conference on computer science and electronics engineering, Hangzhou, pp 647–651
Mar KK (2011) Secured virtual diffused file system for the cloud. In: 6th international conference on internet technology and secured transactions, Abu Dhabi, pp 116–121
Bian J, Seker R (2009) JigDFS: a secure distributed file system. In: IEEE symposium on computational intelligence in cyber security, Nashville, TN, pp 76–82
Sasajima M, Kitamura Y, Naganuma T Toward task ontology-based modeling for mobile phone users activity. In: PID-29
Iyengar A, Cahn R, Garay JA, Jutla C (1998) Design and implementation of a secure distributed data repository, In: Proceedings of the 14th IFIP international information security conference, pp. 123–135(1998)
M. Bilenko, M. Richardson (2011) Predictive client-side proles for personalized advertising. In: KDD’11, proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, California, pp 413–421
Cassel LN, Wolz U (2001) Client side personalization. In: Proceedings of the joint DELOSNSF workshop on personalization and recommender systems in digital libraries. Dublin City University, Dublin
Ceri S, Dolog P, Matera M, Nejdl W (2004) Model-driven design of web applications with client-side adaptation. In: International conference on web engineering, ICWE04, vol 3140. Springer, Munich, pp 201–214
Kay J (2006) Scrutable adaptation: because we can and must. In: Adaptive hypermedia and adaptive web-based systems. Springer, Berlin, pp 11–19
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Atote, B., Zahoor, S., Bedekar, M., Panicker, S. (2018). Proposed Use of Information Dispersal Algorithm in User Profiling. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-3932-4_9
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DOI: https://doi.org/10.1007/978-981-10-3932-4_9
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