In the field of multi-application personalization, several techniques have been proposed to support user modeling. None of them have sufficiently investigated the opportunity for a multi-application profile to evolve over time in order to avoid data inconsistency and the subsequent loss of income for website users and companies. In this paper, we propose a model addressing this issue and we focus in particular on user profile data propagation management, as a way to reduce the amount of inconsistent user profile information over several applications. To evaluate our model, we first extract user profiles using logs of the large real-life AOL search engine. Then, we simulate data propagation along semantically related user information.