Identity management serves as a critical enabler of digital ecosystems, extending across mobile applications, IoT, and emerging platforms like the Metaverse. While identity assurance frameworks offer structured approaches to trust across domains, they often lack mechanisms for dynamic, context-aware, and fine-grained trust evaluation. This paper addresses this gap by implementing the Attribute Level Of Confidence (ALOC) metric, which assesses trust at the level of individual attributes using indicators such as timestamp, revision number, and validity period. The ALOC metric is deployed as a Chrome Extension and tested in a controlled scenario to evaluate its effectiveness in enhancing user control and trust transparency. Results show a 15% improvement in performance, accessibility, and privacy over existing OpenAM-based frameworks. The implementation demonstrates how fine-grained attribute assurance can support selective disclosure and strengthen identity trustworthiness, offering a practical pathway toward more user-centric and privacy-preserving identity management systems.