IDTrace: A Deterministic Digital Footprint Risk Assessment Platform

The widespread adoption of digital identities across numerous online platforms has significantly heightened individuals’ vulnerability to credential exposure and large-scale data breaches. Current breach notification mechanisms remain largely siloed, offering piecemeal alerts without any meaningful interpretation of cumulative risk. This paper introduces IDTrace, a deterministic framework for quantifying digital footprint risk, which consolidates multi-source open-source intelligence (OSINT) and produces a transparent, explainable risk score on a 0–100 scale through severity-weighted deductions and a critical breach ceiling mechanism. To improve throughput and ensure resilience against failure, the system leverages concurrent intelligence retrieval through parallel execution pipelines. Empirical results demonstrate a 37.5% decrease in scan latency via asynchronous processing, linear scalability with respect to computational load, and perfectly consistent deterministic outputs with zero variance across repeated evaluations. A comparative study highlights IDTrace’s advantages in interpretability and holistic exposure modeling over conventional breach detection solutions and probabilistic risk scoring methods. Collectively, IDTrace offers a structured, transparent, and user-centered foundation for cybersecurity risk assessment in an increasingly interconnected digital landscape