- Purpose – To propose a model of a privacy-enhanced catalogue search system (PECSS) in an attempt to address privacy threats to consumers, who search for products and services on the world wide web. Design/methodology/approach – The model extends an agent-based architecture for electronic catalogue mediation by supplementing it with a privacy enhancement mechanism. This mechanism introduces fake queries into the original stream of user queries, in an attempt to reduce the similarity between the actual interests of users (“internal user profile”) and the interests as observed by potential eavesdroppers on the web (“external user profile”). A prototype was constructed to demonstrate the feasibility and effectiveness of the model. Findings – The evaluation of the model indicates that, by generating five fake queries per each original user query, the user's profile is hidden most effectively from any potential eavesdropper. Future research is needed to identify the optimal glossary of fake queries for various clients. The model also should be tested against various attacks perpetrated against the mixed stream of original and fake queries (i.e. statistical clustering). Research limitations/implications – The model's feasibility was evaluated through a prototype. It was not empirically tested against various statistical methods used by intruders to reveal the original queries. Practical implications – A useful architecture for electronic commerce providers, internet service providers (ISP) and individual clients who are concerned with their privacy and wish to minimize their dependencies on third-party security providers. Originality/value – The contribution of the PECSS model stems from the fact that, as the internet gradually transforms into a non-free service, anonymous browsing cannot be employed any more to protect consumers' privacy, and therefore other approaches should be explored. Moreover, unlike other approaches, our model does not rely on the honesty of any third mediators and proxies that are also exposed to the interests of the client. In addition, the proposed model is scalable as it is installed on the user's computer.