A Reputation-based Framework for Honest Provenance Reporting
A Reputation-based Framework for Honest Provenance Reporting
复制标题
DOI：
10.1145/3507908
复制
发表时间：
2022-02
影响因子：
5.3
通讯作者：
L. Barakat;Phillip Taylor;N. Griffiths;S. Miles
中科院分区：
文献类型：
--
作者：
L. Barakat;Phillip Taylor;N. Griffiths;S. Miles
鉴于基于服务的物联网系统的分布式、异构和动态特性，捕获服务提供的环境数据对于理解流程和跟踪输出如何产生变得越来越重要，从而使客户能够对未来的交互伙伴做出更明智的决策。虽然服务提供者是这种情况数据的主要来源，但他们往往不愿意公布这些数据，例如，由于所需的成本和努力，或为了保护他们的利益。作为回应，本文介绍了一个基于声誉的框架，智能软件代理的指导下，支持供应商的真实情况信息的共享。在这个框架中，评估代理，代表客户端，排名和选择服务提供商根据声誉，而供应商代理，代表服务提供商，从环境中学习，并调整供应商的情况下提供政策的方向，增加供应商的利润相对于感知声誉。评估代理人采用的声誉评估模型的新奇在于，除了现有声誉计划通常采用的其他因素之外，评估代理人是否揭示了其服务条款背后的真实情况数据，从而影响提供者的声誉分数。该框架的有效性通过基于代理的模拟，包括对一些攻击的鲁棒性，对消防作为基准信誉模型的比较性能分析证明。
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 article 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.