Tulungan: A concensus-independent reputation system for collaborative web filtering systems

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...

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Main Authors: Pantola, Alexis V., Festin, Susan Pancho, Salvador, Florante R.
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Published: Animo Repository 2011
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/9255
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-109382023-05-11T02:34:56Z Tulungan: A concensus-independent reputation system for collaborative web filtering systems Pantola, Alexis V. Festin, Susan Pancho Salvador, Florante R. 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. 2011-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/9255 Faculty Research Work Animo Repository Online reputation management Information filtering systems Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Online reputation management
Information filtering systems
Computer Sciences
spellingShingle Online reputation management
Information filtering systems
Computer Sciences
Pantola, Alexis V.
Festin, Susan Pancho
Salvador, Florante R.
Tulungan: A concensus-independent reputation system for collaborative web filtering systems
description 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.
format text
author Pantola, Alexis V.
Festin, Susan Pancho
Salvador, Florante R.
author_facet Pantola, Alexis V.
Festin, Susan Pancho
Salvador, Florante R.
author_sort Pantola, Alexis V.
title Tulungan: A concensus-independent reputation system for collaborative web filtering systems
title_short Tulungan: A concensus-independent reputation system for collaborative web filtering systems
title_full Tulungan: A concensus-independent reputation system for collaborative web filtering systems
title_fullStr Tulungan: A concensus-independent reputation system for collaborative web filtering systems
title_full_unstemmed Tulungan: A concensus-independent reputation system for collaborative web filtering systems
title_sort tulungan: a concensus-independent reputation system for collaborative web filtering systems
publisher Animo Repository
publishDate 2011
url https://animorepository.dlsu.edu.ph/faculty_research/9255
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