A Reputation-based Framework for Honest Provenance Reporting

Abstract

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents , acting on behalf of clients, rank and select service providers according to reputation, while provider agents , acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model.

Reasoning and interaction for social artificial intelligence

“…Philosophers these last three thousand years, have spent most of their efforts studying propositions, statements that purport to make factual representations about the world. 3 Many interactions, perhaps even most, between humans or between machines are not propositional in nature, however, but involve utterances over actions such as requests, promises, commands, etc. It is the semantics and pragmatics of protocols for dialogue over action that are primarily the focus here.…”

“…Addressing the challenge of ensuring reliable data is available from which to detect unanticipated emergent behaviour, the team first built upon their past research in data provenance, providing interoperable standards for capturing causal descriptions of behaviour across distributed systems, and then explored corroboration between agents' accounts as a way of assessing reliability of individual agents' claims [3]. Measuring the reliability of reporting through corroboration does not in itself ensure that agents will behave reliably: there also needs to be a way of incentivising good reporting behaviour.…”

User-Centric Privacy for Identity Federations Based on a Recommendation System

“…Building these provider reputation systems and deciding the weights to be assigned to each evaluator's score is beyond the scope of this paper. However, it seems essential to include the possibility of taking into account this type of reputation system in the PAdv since in complex contexts involving a multitude of providers such as Cloud Computing [48] or IoT [49,50] (both similar to identity federations in terms of distribution and heterogeneity) it is a trend that is gaining more and more strength [51], even recurring to decentralised reputation systems based on blockchain [52].…”