Fab: content-based, collaborative recommendation
Communications of the ACM
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
TRUSTe: an online privacy seal program
Communications of the ACM
Anonymous Web transactions with Crowds
Communications of the ACM
Proceedings of the 6th international conference on Intelligent user interfaces
XLibris: an automated library research assistant
Proceedings of the 6th international conference on Intelligent user interfaces
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
A new privacy model for hiding group interests while accessing the Web
Proceedings of the 2002 ACM workshop on Privacy in the Electronic Society
A New Privacy Model for Web Surfing
NGITS '02 Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems
How to Make Personalized Web Browising Simple, Secure, and Anonymous
FC '97 Proceedings of the First International Conference on Financial Cryptography
Proceedings of the First International Workshop on Information Hiding
Anonymous Connections and Onion Routing
SP '97 Proceedings of the 1997 IEEE Symposium on Security and Privacy
Preserving user's privacy in web search engines
Computer Communications
Usability engineering for the adaptive web
The adaptive web
Optimized query forgery for private information retrieval
IEEE Transactions on Information Theory
A privacy-preserving architecture for the semantic web based on tag suppression
TrustBus'10 Proceedings of the 7th international conference on Trust, privacy and security in digital business
Exploiting social networks to provide privacy in personalized web search
Journal of Systems and Software
Optimal tag suppression for privacy protection in the semantic Web
Data & Knowledge Engineering
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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PRAW, a privacy model proposed recently, is aimed at protecting Web surfers' privacy by hiding their interests, i.e., their profiles. PRAW generates several faked transactions for each real user's transaction. The faked transactions relate to various fields of interest in order to confuse eavesdroppers attempting to derive users' profiles. They provide eavesdroppers with inconsistent data for the profile generation task. PRAW creates two profiles, a real user profile and a faked one aimed at confusing eavesdroppers. In this paper we demonstrate that the number of user transactions used for user profile generation significantly affects PRAW's ability to hide users' interests. We claim that there exists an optimal profile update rate for every user according to his surfing behavior. A system implementing PRAW needs to learn, for each specific user, the user's behavior, and dynamically adjust the optimal number of transactions that should be used to generate the user profile.