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

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>01/2014
<mark>Journal</mark>Applied Intelligence
Issue number1
Number of pages16
Pages (from-to)127-142
Early online date1/06/13
<mark>Original language</mark>English


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. Nowadays, 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 proles 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.

Bibliographic note

The original publication is available at www.link.springer.com