Trust and Reputation Management Systems (TRMSs) are critical for the modern web, yet their reliance on subjective user ratings or narrow Quality of Service (QoS) metrics lacks objective grounding. Concurrently, while regulatory frameworks like GDPR and HIPAA provide objective behavioral standards, automated compliance auditing has been limited to coarse, binary (pass/fail) outcomes. This paper bridges this research gap by operationalizing regulatory compliance as a quantitative and dynamic trust metric through our novel automated compliance engine (ACE). ACE first formalizes legal and organizational policies into a verifiable, obligation-centric logic. It then continuously audits system event logs against this logic to detect violations. The core of our contribution is a quantitative model that assesses the severity of each violation along multiple dimensions, including its Volume, Duration, Breadth, and Criticality, to compute a fine-grained, evolving compliance score. We evaluate ACE on a synthetic hospital dataset, demonstrating its ability to accurately detect a range of complex HIPAA and GDPR violations and produce a nuanced score that is significantly more expressive than traditional binary approaches. This work enables the development of more transparent, accountable, and resilient TRMSs on the Web.

翻译：信任与声誉管理系统（TRMSs）对现代网络至关重要，但它们依赖主观用户评分或狭义的服务质量（QoS）指标，缺乏客观基础。同时，虽然GDPR和HIPAA等监管框架提供了客观的行为标准，但自动化合规审计仅限于粗略的二元（通过/未通过）结果。本文通过我们新颖的自动化合规引擎（ACE），将监管合规性操作化为一种量化的动态信任度量，从而弥合了这一研究空白。ACE首先将法律和组织政策形式化为一种可验证的、以义务为中心的逻辑。然后，它持续审计系统事件日志与该逻辑的匹配情况以检测违规行为。我们贡献的核心是一个定量模型，该模型从多个维度评估每次违规的严重程度，包括其体量、持续时间、广度和关键性，从而计算出细粒度且不断演化的合规性分数。我们在一个合成的医院数据集上评估了ACE，展示了其准确检测一系列复杂HIPAA和GDPR违规行为的能力，并生成了一个比传统二元方法更具表达力的细微分数。这项研究工作为在网络上开发更透明、更负责任且更具韧性的TRMSs提供了支持。