Consumer privacy concerns about Internet marketing
Communications of the ACM
Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
Internet privacy concerns confirm the case for intervention
Communications of the ACM
Privacy in e-commerce: examining user scenarios and privacy preferences
Proceedings of the 1st ACM conference on Electronic commerce
Enhancing privacy and trust in electronic communities
Proceedings of the 1st ACM conference on Electronic commerce
A methodology for workload characterization of E-commerce sites
Proceedings of the 1st ACM conference on Electronic commerce
Characterizing reference locality in the WWW
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
HTTP Cookies: Standards, privacy, and politics
ACM Transactions on Internet Technology (TOIT)
Privacy-enhancing technologies for the Internet
COMPCON '97 Proceedings of the 42nd IEEE International Computer Conference
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Masks: Bringing Anonymity and Personalization Together
IEEE Security and Privacy
WebMedia '06 Proceedings of the 12th Brazilian Symposium on Multimedia and the web
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The conflict between Web service personalization and privacy is a challenge in the information society. In this paper we address this challenge by introducing MASKS, an architecture that provides data on the users' interests to Web services, without violating their privacy. The proposed approach hides the actual identity of users by classifying them into groups, according to their interests exhibited during the interaction with a Web service. By making requests on behalf of a group, instead of an individual user, MASKS provides relevant information to the Web services, without disclosing the identity of the users. We have implemented and tested a grouping algorithm, based on categories defined by the semantic tree of DMOZ. We used access logs from actual e-commerce sites to evaluate the grouping algorithm. Our tests show that 64% of the requests made to the e-commerce service could be grouped into meaningful categories. This indicates that the e-commerce sites could use the information provided by MASKS to do personalization of services, without having access to the individual users in the groups.