Invoice factoring is a very useful tool for developing businesses that face liquidity problems. The main property that a factoring system needs to fulfill is to prevent an invoice from being factored twice. In order to prevent double factoring, many factoring ecosystems use one or several centralized entities to register factoring agreements. However, this puts a lot of power in the hands of these centralized entities and makes it difficult for users to dispute situations in which factoring data is unavailable, wrongly recorded or manipulated by negligence or on purpose. In this article, we propose an architecture for invoice factoring registration based on a public blockchain. To solve the aforementioned drawbacks, we replace the trusted third parties for factoring registration with a smart contract. Using a smart contract, we record digital evidence of the terms and conditions of factoring agreements in explicit detail, allowing auditability and dispute resolution. Relevant information is highly available on the blockchain while its privacy is protected. The registration is optimal, since it needs only one blockchain transaction and one key-value storage per invoice factoring.
Uncertainty and its imposed risk have significant impacts on decision-making. However, both are disregarded in many trust-based applications. In this paper, we propose a risk-aware approach to explicitly take uncertainty of trust and its effects into account. Our approach consists of a trust, a confidence, and a risk model. We do not prescribe a specific trust model, and any probabilistic trust model can be empowered by our approach. The confidence model calculates the uncertainty of the trust model in the form of a confidence interval, and is independent of the inner-workings of the trust model. This interval is used by the utility-based risk model which assesses the effects of uncertainty on trust-based decisions. We evaluated our approach by a four-state HMM-based simulated trustee, and employed the Beta, HMM and evidence-based trust models. We proposed and compared different methods for calculating confidence intervals, as well as methods for determining the risk and opportunity of a trust-based interaction. The results demonstrate how our approach should be used to improve the correctness of decision-making in trust-based applications. According to the statistical analysis of the simulation results, confidence intervals can properly represent the trust value and its uncertainty, and strongly improve trust-based decisions.
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