Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings

In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different...

Full description

Saved in:
Bibliographic Details
Main Author: Zhang, Lizi
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: 2012
Subjects:
Online Access:http://hdl.handle.net/10356/49138
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-49138
record_format dspace
spelling sg-ntu-dr.10356-491382023-03-03T20:44:57Z Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings Zhang, Lizi Ng Wee Keong School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different trust models for handling unfair ratings have been claimed by their designers, recently it is argued that these models are vulnerable to more intelligent attacks, and there is an urgent demand that the robustness of the existing trust models has to be evaluated in a more comprehensive way. In this work, we classify the existing trust models into two broad categories and propose an extendable e-marketplace testbed to evaluate their robustness against different unfair rating attacks comprehensively. On top of highlighting the robustness of the existing trust models for handling unfair ratings is far from what they were claimed to be, we further propose and validate a novel combination mechanism for the existing trust models, Discount-then-Filter, to notably enhance their robustness against the investigated attacks. Bachelor of Engineering (Computer Science) 2012-05-15T03:53:36Z 2012-05-15T03:53:36Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/49138 en Nanyang Technological University 62 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Zhang, Lizi
Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
description In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different trust models for handling unfair ratings have been claimed by their designers, recently it is argued that these models are vulnerable to more intelligent attacks, and there is an urgent demand that the robustness of the existing trust models has to be evaluated in a more comprehensive way. In this work, we classify the existing trust models into two broad categories and propose an extendable e-marketplace testbed to evaluate their robustness against different unfair rating attacks comprehensively. On top of highlighting the robustness of the existing trust models for handling unfair ratings is far from what they were claimed to be, we further propose and validate a novel combination mechanism for the existing trust models, Discount-then-Filter, to notably enhance their robustness against the investigated attacks.
author2 Ng Wee Keong
author_facet Ng Wee Keong
Zhang, Lizi
format Final Year Project
author Zhang, Lizi
author_sort Zhang, Lizi
title Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
title_short Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
title_full Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
title_fullStr Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
title_full_unstemmed Safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
title_sort safeguarding e-commerce against advisor cheating behaviors : towards more robust trust models for handling unfair ratings
publishDate 2012
url http://hdl.handle.net/10356/49138
_version_ 1759855806645272576