Skip to main content
SpringerLink
Log in
Book cover

Social Collective Intelligence pp 159–184Cite as

An Auditable Reputation Service for Collective Adaptive Systems

  • Chapter
  • First Online:

Part of the book series: Computational Social Sciences ((CSS))

Abstract

The reputation of subject is a measure of a community’s opinion about that subject. A subject’s reputation plays a core role in communities within Collective Adaptive Systems (CAS) and can influence a community’s perception and their interactions with the subject. Their reputation can also affect computational activities within a system. While reputation is frequently used in CAS, there is a lack of agreed methods for its use, representation, and auditability. The aim of this chapter is to investigate key facets of an auditable reputation service for CAS, we contribute: Use cases for reputation and provenance in CAS, which are categorised into functional, auditable, privacy and security, and administrative; and a RESTful Reputation API, which allows users access to subject feedback and to access feedback reports and reputation measures.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    In general a CAS may be constituted only by artificial agents, with no humans, and thus with no social elements.

  2. 2.

    Uniquely identifying participants does not require or imply the use of any personally identifiable information, which would connect the participant to the real person.

  3. 3.

    A comprehensive list of the systems identified can be found at: http://sociam.org/social-machines.

  4. 4.

    http://googleblog.blogspot.co.uk/2009/08/bright-side-of-sitting-in-traffic.html.

  5. 5.

    http://collabmap.org/.

  6. 6.

    ProvStore: https://provenance.ecs.soton.ac.uk/store/.

  7. 7.

    prov-O: http://www.w3.org/TR/prov-o/.

  8. 8.

    The hash indicates that this entity isn’t stored in memory.

References

  1. Berners-Lee, T.: Design Issues: Cleaning up the User Interface. W3C Note, World Wide Web Consortium (1997). URL http://www.w3.org/DesignIssues/UI.html

  2. Berners-Lee, T., Fischetti, M., Foreword By-Dertouzos, M.L.: Weaving the Web: The original design and ultimate destiny of the World Wide Web by its inventor. HarperInformation (2000)

    Google Scholar 

  3. Chirita, P.A., Nejdl, W., Schlosser, M.T., Scurtu, O.: Personalized reputation management in p2p networks. In: ISWC Workshop on Trust, Security, and Reputation on the Semantic Web (2004)

    Google Scholar 

  4. Gal, K., et al.: Rideshare: A smart society application. Web Semantics: Science, Services and Agents on the World Wide Web (2014). URL https://docs.google.com/document/d/1cNhX-sW8pVyG5XYzrDNXzrn8R-NeKrzGszkDYJ3KwKQ/edit?invite=CPnQk7sG&pli=1

  5. Gil, Y., Artz, D.: Towards content trust of web resources. Web Semant. Sci. Serv. Agents World Wide Web 5(4), 227–239 (2007). DOI http://dx.doi.org/10.1016/j.websem.2007.09.005. URL http://www.sciencedirect.com/science/article/pii/S1570826807000376. ¡ce:title¿World Wide Web Conference 2006Semantic Web Track¡/ce:title¿

  6. Golbeck, J.: Trust on the world wide web: A survey. Found. Trends Web Sci. 1(2), 131–197 (2006). DOI 10.1561/1800000006. URL http://dx.doi.org/10.1561/1800000006

  7. Golbeck, J.: Introduction to computing with social trust. In: Computing with Social Trust, pp. 1–5. Springer, London (2009)

    Google Scholar 

  8. Golbeck, J., Hendler, J.: Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based Social Networks. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) Engineering Knowledge in the Age of the Semantic Web, Proceedings of the 14th International Conference, EKAW 2004, Lecture Notes in Computer Science, vol. 3257, pp. 116–131. Springer, Berlin/Heidelberg (2004). DOI 10.1007/978-3-540-30202-5_8. URL http://dx.doi.org/10.1007/978-3-540-30202-5_8

  9. Golbeck, J.A.: Computing and applying trust in web-based social networks. Ph.D. thesis, College Park, MD, USA (2005). AAI3178583

    Google Scholar 

  10. Hua, H., Tilmes, C., Zednik (eds.), S., Moreau, L.: PROV-XML: The PROV XML Schema. W3C Working Group Note NOTE-prov-xml-20130430, World Wide Web Consortium (2013). URL http://www.w3.org/TR/2013/NOTE-prov-xml-20130430/

  11. Huang, B., Kimmig, A., Getoor, L., Golbeck, J.: A flexible framework for probabilistic models of social trust. In: Social Computing, Behavioral-Cultural Modeling and Prediction, pp. 265–273. Springer, Berlin/Heidelberg (2013)

    Google Scholar 

  12. Huynh, T.D.: Trust and reputation in open multi-agent systems (2006). URL http://eprints.soton.ac.uk/262759/

  13. Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007). DOI 10.1016/j.dss.2005.05.019. URL http://dx.doi.org/10.1016/j.dss.2005.05.019

  14. Koutrouli, E., Tsalgatidou, A.: Reputation-based trust systems for p2p applications: Design issues and comparison framework. In: Proceedings of the Third International Conference on Trust, Privacy, and Security in Digital Business, TrustBus’06, pp. 152–161. Springer, Berlin, Heidelberg (2006). DOI 10.1007/11824633_16. URL http://dx.doi.org/10.1007/11824633_16

  15. Lebo, T., Sahoo, S., McGuinness (eds.), D., Behajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: PROV-O: The PROV Ontology. W3C Recommendation REC-prov-o-20130430, World Wide Web Consortium (2013). URL http://www.w3.org/TR/2013/REC-prov-o-20130430/

  16. Li, X., Lebo, T., McGuinness, D.L.: Provenance-based strategies to develop trust in semantic web applications. In: McGuinness, D.L., Michaelis, J., Moreau, L. (eds.) Provenance and Annotation of Data and Processes, Lecture Notes in Computer Science, vol. 6378, pp. 182–197. Springer, Berlin/Heidelberg (2010). DOI 10.1007/978-3-642-17819-1_21. URL http://dx.doi.org/10.1007/978-3-642-17819-1_21

  17. Liu, L., Munro, M.: Systematic analysis of centralized online reputation systems. Decis. Support Syst. 52(2), 438–449 (2012). DOI 10.1016/j.dss.2011.10.003. URL http://dx.doi.org/10.1016/j.dss.2011.10.003

  18. Moreau, L.: Provenance-based reproducibility in the semantic web. Web Semant. Sci. Serv. Agents World Wide Web 9(2), 202–221 (2011). URL http://eprints.soton.ac.uk/271992/

  19. Moreau, L., Groth, P.: Provenance: An Introduction to PROV. Morgan and Claypool, San Rafael, CA, US (2013). URL http://dx.doi.org/10.2200/S00528ED1V01Y201308WBE007

  20. Moreau, L., Missier (eds.), P., Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers, J., Sahoo, S., Tilmes, C.: PROV-DM: The PROV Data Model. W3C Recommendation REC-prov-dm-20130430, World Wide Web Consortium (2013). URL http://www.w3.org/TR/2013/REC-prov-dm-20130430/

  21. Moreau, L., Missier (eds.), P., Cheney, J., Soiland-Reyes, S.: PROV-N: The Provenance Notation. W3C Recommendation REC-prov-n-20130430, World Wide Web Consortium (2013). URL http://www.w3.org/TR/2013/REC-prov-n-20130430/

  22. Pinyol, I., Sabater-Mir, J.: Arguing about social evaluations: From theory to experimentation. Int. J. Approximate Reason. 54(5), 667–689 (2013). DOI http://dx.doi.org/10.1016/j.ijar.2012.11.006. URL http://www.sciencedirect.com/science/article/pii/S0888613X1200196X

  23. Pinyol, I., Sabater-Mir, J.: Computational trust and reputation models for open multi-agent systems: a review. Artif. Intell. Rev. 40(1), 1–25 (2013). DOI 10.1007/s10462-011-9277-z. URL http://dx.doi.org/10.1007/s10462-011-9277-z

  24. Prat, N., Madnick, S.: Measuring data believability: A provenance approach. In: Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences, HICSS ’08, pp. 393–. IEEE Computer Society, Washington, DC, USA (2008). DOI 10.1109/HICSS.2008.243. URL http://dx.doi.org/10.1109/HICSS.2008.243

  25. Ramchurn, S.D., Huynh, D., Jennings, N.R., et al.: Trust in multi-agent systems. Knowl. Eng. Rev. 19(1), 1–25 (2004)

    Google Scholar 

  26. Ramchurn, S.D., Huynh, T.D., Venanzi, M., Shi, B.: Collabmap: crowdsourcing maps for emergency planning. In: The 5th Annual ACM Web Science Conference, pp. 326–335 (2013). URL http://eprints.soton.ac.uk/350677/

  27. Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) The Semantic Web—ISWC 2003, Lecture Notes in Computer Science, vol. 2870, pp. 351–368. Springer, Berlin/Heidelberg (2003). DOI 10.1007/978-3-540-39718-2_23. URL http://dx.doi.org/10.1007/978-3-540-39718-2_23

  28. Sabater, J., Sierra, C.: Review on computational trust and reputation models. Artif. Intell. Rev. 24(1), 33–60 (2005). DOI 10.1007/s10462-004-0041-5. URL http://dx.doi.org/10.1007/s10462-004-0041-5

  29. Shadbolt, N.R., Smith, D.A., Simperl, E., Kleek, M.V., Yang, Y., Hall, W.: Towards a classification framework for social machines. In: SOCM2013: The Theory and Practice of Social Machines (2013). URL http://eprints.soton.ac.uk/350513/

  30. Shadbolt, N.R., Smith, D.A., Simperl, E., Van Kleek, M., Yang, Y., Hall, W.: Towards a classification framework for social machines. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 905–912. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  31. Smart, P., Simperl, E., Shadbolt, N.: A Taxonomic Framework for Social Machines. In: Miorandi, D., Maltese, E., Rovatsos, M., Nijholt, A., Stewart, J. (eds.) Social Collective Intelligence: Combining the Powers of Humans and Machines to Build a Smarter Society. Springer, New York (2014)

    Google Scholar 

  32. Stein, K., Hess, C.: Does it matter who contributes: A study on featured articles in the german wikipedia. In: Proceedings of the Eighteenth Conference on Hypertext and Hypermedia, HT ’07, pp. 171–174. ACM, New York, NY, USA (2007). DOI 10.1145/1286240.1286290. URL http://doi.acm.org/10.1145/1286240.1286290

  33. von Ahn, L., Maurer, B., Mcmillen, C., Abraham, D., Blum, M.: reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science 321(5895), 1465–1468 (2008). URL http://dx.doi.org/10.1126/science.1160379

  34. Vu, L.H., Aberer, K.: Effective usage of computational trust models in rational environments. ACM Trans. Auton. Adapt. Syst. 6(4), 24:1–24:25 (2011). DOI 10.1145/2019591.2019593. URL http://doi.acm.org/10.1145/2019591.2019593

  35. Weitzner, D.J., Abelson, H., Berners-Lee, T., Feigenbaum, J., Hendler, J., Sussman, G.J.: Information Accountability, pp. 82–87. ACM, New York (2008)

    Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the SmartSociety and SOCIAM projects. The SmartSociety Project is funded by the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n 600854. The SOCIAM Project is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/J017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. The authors would also like to acknowledge Avi Segal, Dimitrios I. Diochnos, Kevin Page, Kobi Gal and Michael Rovatsos, for their work on the ride share application.

Author information

Authors and Affiliations

  1. University of Southampton, University Road, Southampton, SO17 1BJ, UK

    Heather S. Packer, Laura Drăgan & Luc Moreau

Authors
  1. Heather S. Packer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Drăgan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luc Moreau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heather S. Packer .

Editor information

Editors and Affiliations

  1. CREATE-NET, Trento, Italy

    Daniele Miorandi

  2. University of Trento, Trento, Italy

    Vincenzo Maltese

  3. School of Informatics Informatics, The University of Edinburgh, Edinburgh, United Kingdom

    Michael Rovatsos

  4. Faculty EEMCS, University of Twente, Enschede, The Netherlands

    Anton Nijholt

  5. The University of Edinburgh, Edinburgh, United Kingdom

    James Stewart

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Packer, H.S., Drăgan, L., Moreau, L. (2014). An Auditable Reputation Service for Collective Adaptive Systems. In: Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J. (eds) Social Collective Intelligence. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-08681-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08681-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08680-4

  • Online ISBN: 978-3-319-08681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation