Proceedings:CYC1

Wikimania 2007 Taipei :: a Globe in Accord
Jump to navigation Jump to search
Wikimania(80x15).gif

This page is part of the Proceedings of Wikimania 2007 (Index of presentations)

Detecting Bad Mouthing Behavior in Reputation Systems

Creators ChunYang Chen (Academia Sinica), ChengChun Lou (National Taiwan University), Polly Huang (National Taiwan University), LingJyh Chen (Academia Sinica)
Track Technical Infrastructure
License Heckert GNU.png GNU Free Documentation License (details)
About the creators
Chun-Yang Chen (previously known as Kuan-Ta Chen) received his B.S. and M.S. in Computer Science from National Tsing Hua University in 1998 and 2000, respectively. He received his Ph.D. in Electrical Engineering from National Taiwan University in 2006. Since then he joined the Institute of Information Science, Academia Sinica as an assistant research fellow. His research interests include multimedia networking, Internet measurement, network security, and multimedia networking. For more information, please visit http://www.iis.sinica.edu.tw/~cychen/.
Presenters/ChengChun Lou/Biography
Presenters/Polly Huang/Biography
Ling-Jyh Chen was born in Taipei, Taiwan. He received the B.Ed. degree in information and computer education from National Taiwan Normal University in 1998, and the M.S. and Ph.D. degrees in computer science from University of California at Los Angeles in 2002 and 2005 respectively. He joined the Institute of Information Science as assistant research fellow in 2005. His research interests are wireless personal area networks, network protocols, Internet measurements, and ubiquitous computing. For more information, please visit http://www.iis.sinica.edu.tw/~cclljj/
Abstract
In this work, we design two schemes to detect bad mouthing behavior in a prosecution-based reputation system. That is, a group of malicious users team up to bring fake prosecutions against a particular user that the user has done some unacceptable actions, even if the user did not do any thing inappropriate. The preliminary results show that the detection accuracy can be higher than 90% given that the proportion of legitimate users in the community is reasonable (> 30%). [Please refer to the full version at http://www.iis.sinica.edu.tw/~ktchen/pub/badmouth.pdf]
0Missing
1Submitted
2Editing
3Review
4Final edit
5Complete
6Done

Nuvola apps kpdf gray.png
Full text

Acroread gray.png
PDF

Nuvola apps package editors gray.png
Notes

Nuvola apps package edutainment gray.png
Slides

Nuvola apps kmix gray.png
Audio

Nuvola apps kaboodle gray.png
Video

Discuss