Article

Detecting deception in reputation management

Authors:

Munindar P. SinghAuthors Info & Claims

AAMAS '03: Proceedings of the second international joint conference on Autonomous agents and multiagent systems

Pages 73 - 80

https://doi.org/10.1145/860575.860588

Published: 14 July 2003 Publication History

Abstract

We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.

References

[1]

A. Abdul-Rahman and S. Hailes. Supporting trust in virtual communities. In Proceedings of Hawaii International Conference on Systems Science 33, 2000.

Digital Library

[2]

K. Aberer and Z. Despotovic. Managing trust in a peer-2-peer information system. In Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM'01), pages 310--317, 2001.

Digital Library

[3]

K. S. Barber and J. Kim. Belief revision process based on trust: Simulation experiments. In Proceedings of Autonomous Agents '01 Workshop on Deception, Fraud, and Trust in Agent Societies, pages 1--12, May 2001.

[4]

S. Brainov and T. Sandholm. Contracting with uncertain level of trust. In Proceedings of the First International Conference on Electronic Commerce (EC'99), pages 15--21, 1999.

Digital Library

[5]

C. Castelfranchi, R. Conte, and M. Paolucci. Normative reputation and the costs of compliance. Journal of Artificial Societies and Social Simulation, 1(3), 1998.

[6]

C. Castelfranchi and R. Falcone. Principle of trust for MAS: cognitive anatomy, social importance, and quantification. In Proceedings of Third International Conference on MultiAgent Systems, pages 72--79, 1998.

Digital Library

[7]

H. Kautz, B. Selman, and A. Milewski. Agent amplified communication. In Proceedings of the National Conference on Artificial Intelligence, pages 3--9, 1996.

Digital Library

[8]

H. E. Kyburg. Bayesian and non-bayesian evidential updating. Artificial Intelligence, 31:271--293, 1987.

Digital Library

[9]

N. Littlestone and M. K. Warmuth. The weighted majority algorithm. Information and Computation, 108(2):212--261, 1994.

Digital Library

[10]

S. P. Marsh. Formalising Trust as a Computational Concept. PhD thesis, Department of Computing Science and Mathematics, University of Stirling, Apr. 1994.

[11]

L. Mui, M. Mohtashemi, and A. Halberstadt. Notions of reputation in multi-agents systems: a review. In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems, pages 280--287, 2002.

Digital Library

[12]

J. M. Pujol, R. Sanguesa, and J. Delgado. Extracting reputation in multiagent systems by means of social network topology. In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems, pages 467--474, 2002.

Digital Library

[13]

J. Sabater and C. Sierra. Reputation and social network analysis in multiagent systems. In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems, pages 475--482, 2002.

Digital Library

[14]

M. Schillo, P. Funk, and M. Rovatsos. Using trust for detecting deceitful agents in artificial societies. Applied Artificial Intelligence, 14:825--848, 2000.

[15]

S. Sen and N. Sajja. Robustness of reputation-based trust: boolean case. In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems, pages 288--293, 2002.

Digital Library

[16]

G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ, 1976.

[17]

D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393:440--442, June 1998.

[18]

B. Yu and M. P. Singh. A social mechanism of reputation management in electronic communities. In Proceedings of Fourth International Workshop on Cooperative Information Agents, pages 154--165, 2000.

Digital Library

[19]

B. Yu and M. P. Singh. An evidential model of distributed reputation management. In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems, pages 294--301, 2002.

Digital Library

[20]

G. Zacharia and P. Maes. Trust management through reputation mechanisms. Applied Artificial Intelligence, 14:881--908, 2000.

Cited By

Sarkadi Ş(2024)Self-Governing Hybrid Societies and DeceptionACM Transactions on Autonomous and Adaptive Systems10.1145/363854919:2(1-24)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3638549
Weng XPan DJin Y(2024)An Adaptive Distributed Consumer Trust Model for Social CommerceApplied Artificial Intelligence10.1080/08839514.2024.238585738:1Online publication date: 5-Aug-2024
https://doi.org/10.1080/08839514.2024.2385857
Kyei KEady YEsterline AShelton JShen CChang KGamess E(2023)Trust-based Enrollment in a Group in a Distributed SettingProceedings of the 2023 ACM Southeast Conference10.1145/3564746.3587014(120-127)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3564746.3587014
Show More Cited By

Index Terms

Detecting deception in reputation management
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

Recommendations

An evidential model of distributed reputation management
AAMAS '02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1

For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy ...
The Role of Reputation on Trust and Loyalty: A Cross-Cultural Analysis of Tablet E-Tailing

The purpose of this article is to empirically examine the role of online retailer's website reputation on tablet commerce and to compare the trust arbitration between reputation and loyalty in two cultures-Finland and Nigeria. Data was collected from ...
Learning trust strategies in reputation exchange networks
AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

An agent's trust decision strategy consists of the agent's policies for making trust-related decisions, such as who to trust, how trustworthy to be, what reputations to believe, and when to tell truthful reputations. In reputation exchange networks, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

AAMAS '03: Proceedings of the second international joint conference on Autonomous agents and multiagent systems

July 2003

1200 pages

ISBN:1581136838

DOI:10.1145/860575

General Chairs:
Jeffrey S. Rosenschein
Israel
,
Michael Wooldridge
UK
,
Program Chairs:
Tuomas Sandholm
USA
,
Makoto Yokoo
Japan

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 July 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

AAMAS03

Sponsor:

AAMAS03: Second International Conference on Autonomous Agents and Multiagent Systems

July 14 - 18, 2003

Melbourne, Australia

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

208
Total Citations
View Citations
2,193
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sarkadi Ş(2024)Self-Governing Hybrid Societies and DeceptionACM Transactions on Autonomous and Adaptive Systems10.1145/363854919:2(1-24)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3638549
Weng XPan DJin Y(2024)An Adaptive Distributed Consumer Trust Model for Social CommerceApplied Artificial Intelligence10.1080/08839514.2024.238585738:1Online publication date: 5-Aug-2024
https://doi.org/10.1080/08839514.2024.2385857
Kyei KEady YEsterline AShelton JShen CChang KGamess E(2023)Trust-based Enrollment in a Group in a Distributed SettingProceedings of the 2023 ACM Southeast Conference10.1145/3564746.3587014(120-127)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3564746.3587014
Wang NWei D(2022)An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent SystemsApplied Sciences10.3390/app1215763312:15(7633)Online publication date: 28-Jul-2022
https://doi.org/10.3390/app12157633
Tagliaferri M(2022)Reviewing the Case of Online Interpersonal TrustFoundations of Science10.1007/s10699-022-09836-228:1(225-254)Online publication date: 17-Apr-2022
https://doi.org/10.1007/s10699-022-09836-2
Monakhov MMonakhov YTelny A(2021)Methods of Information Processing and Presentation in Peer-to-Peer Online MarketplacesImpact of Disruptive Technologies on the Sharing Economy10.4018/978-1-7998-0361-4.ch003(28-49)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-0361-4.ch003
Kyei KNick WEsterline A(2021)A Method for Calculating Trustworthiness in Social NetworksSoutheastCon 202110.1109/SoutheastCon45413.2021.9401947(01-06)Online publication date: 10-Mar-2021
https://doi.org/10.1109/SoutheastCon45413.2021.9401947
Ji SBao CSun G(2021)An Evolutionary-Based Game Model of Honest and Dishonest SellersAdvances in Intelligent Data Analysis and Applications10.1007/978-981-16-5036-9_9(85-94)Online publication date: 26-Nov-2021
https://doi.org/10.1007/978-981-16-5036-9_9
Aldini ATagliaferri M(2020)Logics to Reason Formally About Trust Computation and ManipulationEmerging Technologies for Authorization and Authentication10.1007/978-3-030-39749-4_1(1-15)Online publication date: 25-Jan-2020
https://doi.org/10.1007/978-3-030-39749-4_1
Parhizkar ENikravan MZilles S(2019)Indirect trust is simple to establishProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367488(3216-3222)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367488
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents