Masks: Managing anonymity while sharing knowledge to servers

Abstract

This work presents an architecture that allows users to enhance their privacy control over the computational environment. Web privacy is a topic that is raising, nowadays, many discussions. Usually, people do not know how their privacy can be violated or what can be done to protect it. Among the generated conflicts, we would like to show up the one that happens between privacy and personalization: by one side, users appreciate the idea of receiving personalized services and do not approve the collection, tracing and analysis of their actions; by the other side, personalization services need this type of information in order to profile their users. The architecture presented in this article helps users to understand better how their privacy can be invaded and, at the same time, gives them a better control of their privacy, through anonymity, without preventing them from receiving personalized services. © 2004 by Springer Science+Business Media Dordrecht.