TULUNGAN: A Consensus-Independent Reputation System for Collaborative Web Filtering Systems - Pantola, Alexis V.; Pancho-Festin, Susan; Salvador, Florante (Science Diliman: A Journal of Pure and Applied Sciences; Vol 23 No 2 (2011), 2012)
Web filtering systems allow or prohibit access to websites based on categories (e.g., pornography, violence, sports, etc.). Categorization of websites can be done automatically or manually. Automatic categorization is prone to under- and over-blocking. On the other hand, manual approach is typically performed by a limited number of people making it not scalable. Collaborative web filtering systems, a variation of manual categorization, allow anyone to categorize websites in order to determine which domain these sites belong (e.g., pornography, violence, sports, etc.). This attempts to solve the scalability issue of the typical manual method. The approach offered by collaborative web filtering relies heavily on the contribution of users in order to make the system scalable and less prone to errors. However, its success is greatly dependent on user cooperation. To promote cooperation, reputation system can be used in web filtering. A previous study called Rater-Rating promotes cooperation and explores the use of a user-driven reputation system that measures both the contributor and rater reputation of users of a collaborative web system. However, Rater-Rating is consensus dependent. If the number of malicious users are more than their good counterparts, the reputation system can be defeated. In other words, the system can mistakenly give malicious users a high reputation value. This study discusses a reputation system called Tulungan that is consensus-independent. It can detect the presence of malicious users even if the number of their good counterparts are fewer. A simulation result that compares the effectiveness of Tulungan relative to Rater-Rating is presented in this paper. The simulation shows that Tulungan is still effective even with 25% good users while Rater-Rating requires at least 50% good users to be effective. Keywords: Reputation System Web Filtering