Adaptive Trust and Risk-Aware Data Usage Control for Privacy-Preserving Edge-Cloud Analytics

Abstract

Edge-cloud analytics enables enterprises to process large volumes of operational data across distributed edge nodes, cloud platforms, and external analytics services. Although this model improves scalability, latency, and analytical capability, it also introduces privacy risks such as unauthorized secondary use, excessive disclosure, cross-domain data exposure, and uncontrolled retention. Conventional accesscontrol mechanisms such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) mainly focus on initial authorization and provide limited support for continuous data usage governance after access is granted. To overcome these shortcomings, this paper proposes an Adaptive Trust and Risk-Aware Data Usage Control Framework (ATR-DUCF) for privacy-preserving edge-cloud analytics. The proposed framework combines metadata-aware policy binding, requester trust-based evaluation, privacy risk scoring, adaptive compliance decision-making, and transformation-based enforcement. Attr-DUCF differs from an allow-or-deny rule by determining the decision for an analytics request based on the trust level, privacy risk, context, and (where possible) enforceable policy obligations. Experimental results show that the proposed framework achieves better compliance, decision accuracy, and data leakage prevention than RBAC, ABAC, and Static DUCF baselines, with a reasonable decision latency and operational overhead.