Strategies for avoiding preference profiling in agent-based e-commerce environments

Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Privacy is of great concern and preserving users' privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a well-known threat to users' privacy. Specifically, we review strategies for customers' agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.This research work is supported by the Spanish Ministry of Science and Innovation in the scope of the Research Projects TSI-020302-2010-129, TIN2011-28335-C02-02 and through the Fundacion Seneca within the Program 04552/GERM/06.Serrano, E.; Such, JM.; Botia, JA.; García Fornes, AM. (2014). Strategies for avoiding preference profiling in agent-based e-commerce environments. Applied Intelligence. 40(1):127-142. https://doi.org/10.1007/s10489-013-0448-2127142401Abedin F, Chao K, Godwin N, Arochena H (2009) Preference ordering in agenda based multi-issue negotiation for service level agreement. In: International conference on Advanced Information Networking and Applications Workshops, 2009. WAINA’09. IEEE Press, New York, pp 19–24Aïmeur E, Brassard G, Onana FSM (2005) Privacy-preserving physical delivery in electronic commerce. 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