Skip to main content

Trust Level Evaluation Engine for Dynamic Trust Assessment with Reference to Subjective Logic

  • Conference paper
  • First Online:
Trust Management XIV (IFIPTM 2023)

Abstract

We propose an architectural design for a Trust Level Evaluation Engine. The engine is meant to work in a complex and dynamic environment of potentially untrustworthy sources of information where the situational knowledge is partial and subjective from the viewpoint of the information source, thus potentially inconsistent and contradictory. Consistently with a Zero-Trust approach, no initial trust between nodes should be assumed, since a decision-making module shall nevertheless figure out its level of confidence about the truth of a proposition over the reality. Our design is theory-agnostic and can be instantiated on different mathematical subjective model theories, but we demonstrate its feasibility by mapping it on the Subjective Logic. We also discuss critical design choices and algorithmic details that are only partially addressed in the abstract description of the theory, and we demonstrate how the engine effectively works on large and complex subjective trust networks. Additionally, we offer a proof-of-concept implementation to showcase the proposed architecture’s ability to handle intricate and complex networks.

T. Dimitrakos—The authors from SnT/UL authors are listed alphabetically.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In reference to Bayesian networks, as well as, to Markov chains, the network represents the dependencies between atomic components.

  2. 2.

    There may a few meta-operators depending on the nature of the dependencies, for instance, in the SL there is a distinction between deduction and abduction.

  3. 3.

    This is in fact the case for SL.

  4. 4.

    Here, \(\sqcap \) is the SL operator conjunction of opinions, and \(\oplus \) and \(\otimes \), resp., for cumulative opinion fusion and trust discount; these symbols are not any more names, but functions that can be called and executed on trust values, e.g., \(\oplus (v,v')\) returns a trust value \(v''\).

  5. 5.

    https://www.wolfram.com/language/.

  6. 6.

    The code, is roughly 1500 lines of code in Python, 800 input cells in Wolfram.

  7. 7.

    We did not find any previous work that discusses this claim, but it can be easily proved that the worst case happens for a DSPG of n nodes that is a series composition of n/2 parallel graphs of tow nodes and two edges each. Here, the number of different series-parallel paths is \(2^{n/2}\).

References

  1. Akhuseyinoglu, N.B., Karimi, M., Abdelhakim, M., Krishnamurthy, P.: On automated trust computation in iot with multiple attributes and subjective logic. In: Proceedings of 2020 IEEE 45th Conference on Local Computer Networks (LCN), pp. 267–278. IEEE, Sydney (2020)

    Google Scholar 

  2. Alur, R., Stanford, C., Watson, C.: A robust theory of series parallel graphs, vol. 7, pp. 277–289. Association for Computing Machinery, New York (2023)

    Google Scholar 

  3. Bodlaender, H.L., de Fluiter, B.: Parallel algorithms for series parallel graphs. In: Diaz, J., Serna, M. (eds.) ESA 1996. LNCS, vol. 1136, pp. 277–289. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61680-2_62

    Chapter  Google Scholar 

  4. Cheng, M., Nazarian, S., Bogdan, P.: There is hope after all: quantifying opinion and trustworthiness in neural networks. Front. Artif. Intell. 3, 54 (2020)

    Article  Google Scholar 

  5. Cheng, M., Sun, T., Nazarian, S., Bogdan, P.: Trustworthiness evaluation and trust-aware design of CNN architectures. In: Proceedings 1st Conference on Lifelong Learning Agents, pp. 1086–1102. PMLR, Montreal (2022)

    Google Scholar 

  6. Cheng, M., Yin, C., Zhang, J., Nazarian, S., Deshmukh, J., Bogdan, P.: A general trust framework for multi-agent systems. In: Proceedings of 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pp. 332–340 (2021)

    Google Scholar 

  7. Garlichs, K., Willecke, A., Wegner, M., Wolf, L.C.: Trip: misbehavior detection for dynamic platoons using trust. In: Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 455–460 (2019)

    Google Scholar 

  8. Jøsang, A.: A logic for uncertain probabilities. Int. J. Uncertain. Fuzz. Knowl.-Based Syst. 9(03), 279–311 (2001)

    Article  MathSciNet  Google Scholar 

  9. Jøsang, A.: Subjective Logic. Springer, Heidelberg (2016)

    Google Scholar 

  10. Jøsang, A., Pope, S.: Dempster’s rule as seen by little colored balls. Comput. Intell. 28(4), 453–474 (2012)

    Article  MathSciNet  Google Scholar 

  11. Jøsang, A., Wang, D., Zhang, J.: Multi-source fusion in subjective logic. In: Proceedings of 20th International Conference on Information Fusion (Fusion) (2017)

    Google Scholar 

  12. Kurdi, H., Alshayban, B., Altoaimy, L., Alsalamah, S.: TrustyFeer: a subjective logic trust model for smart city peer-to-peer federated clouds. Wirel. Commun. Mobile Comput. 2018 (2018)

    Google Scholar 

  13. Liu, B.: A survey on trust modeling from a Bayesian perspective. Wirel. Pers. Commun. 112(2), 1205–1227 (2020)

    Article  Google Scholar 

  14. Othman, H., Gudes, E., Gal-Oz, N.: Advanced flow models for computing the reputation of internet domains. In: Steghöfer, J.-P., Esfandiari, B. (eds.) IFIPTM 2017. IAICT, vol. 505, pp. 119–134. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59171-1_10

    Chapter  Google Scholar 

  15. Valdes, J., Tarjan, R.E., Lawler, E.L.: The recognition of series parallel digraphs. SIAM J. Comput. 11(2), 298–313 (1982)

    Article  MathSciNet  Google Scholar 

  16. Wylde, A.: Zero trust: never trust, always verify. In: Proceedings of the International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), pp. 1–4 (2021)

    Google Scholar 

Download references

Acknowledgements

This research is partially funded by the HORIZON CONNECT project under EU grant agreement no. 101069688.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ana Petrovska .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Petrovska, A. et al. (2024). Trust Level Evaluation Engine for Dynamic Trust Assessment with Reference to Subjective Logic. In: Muller, T., Fernandez-Gago, C., Ceolin, D., Gudes, E., Gal-Oz, N. (eds) Trust Management XIV. IFIPTM 2023. IFIP Advances in Information and Communication Technology, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-031-76714-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-76714-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-76713-5

  • Online ISBN: 978-3-031-76714-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics