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.
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Notes
- 1.
In reference to Bayesian networks, as well as, to Markov chains, the network represents the dependencies between atomic components.
- 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.
This is in fact the case for SL.
- 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.
- 6.
The code, is roughly 1500 lines of code in Python, 800 input cells in Wolfram.
- 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 \(2^{n/2}\).
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Acknowledgements
This research is partially funded by the HORIZON CONNECT project under EU grant agreement no. 101069688.
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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
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