Agent-based electronic commerce is known to oer many advan-
tages to users. However, very few studies have been devoted to deal with
privacy issues in this domain. Nowadays, privacy is of great concern and pre-
serving users' privacy plays a crucial role to promote their trust in agent-based
technologies. In this paper, we focus on preference proling, which is a well-
known threat to users' privacy. Specically, 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 ecacy of each of these strategies and discuss their suitability in dierent
situations. Our experimental results show that customers can improve their
privacy notably with these strategies.