Compliance as a Trust Metric

TL;DR

This paper introduces ACE, an automated compliance engine that quantifies legal and organizational adherence as a dynamic trust metric, enabling more nuanced and objective trust assessments in web systems.

Contribution

It formalizes compliance policies into verifiable logic and develops a model to compute a detailed compliance score based on violation severity, advancing trust metrics beyond binary measures.

Findings

ACE accurately detects complex HIPAA and GDPR violations

Produces a nuanced, multi-dimensional compliance score

Outperforms traditional binary compliance approaches

Abstract

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.