Zero-Trust Bilateral Edge Service Trading with Deposit-Refund Regulation for Runtime Compliance

TL;DR

ZEBRIS is a framework for privacy-sensitive edge service trading that ensures compliance, improves social welfare, and reduces costs through deposit-refund regulation and a zero-trust bilateral clearing mechanism.

Contribution

It introduces a novel zero-trust bilateral trading model with deposit-refund regulation that jointly considers runtime compliance, security, and resource allocation in edge markets.

Findings

ZEBRIS enhances social welfare and compliance robustness.

It reduces service delay and privacy-risk-weighted costs.

The framework satisfies key economic properties like individual rationality.

Abstract

Privacy-sensitive edge services necessitate optimizing diverse-type resource scheduling to support trustworthy provisioning within a zero-trust security framework. However, existing studies rarely model how runtime compliance jointly affects bilateral clearing, ex-post settlement, and future seller eligibility in dynamic edge markets. To address this issue, we propose ZEBRIS, a zero-trust bilateral edge service trading framework with deposit-refund regulation for privacy-sensitive services. Specifically, edge provisioning is modeled as a trading form of zero-trust-compliant service packages, where the buyer-side effective valuation captures service value, delay penalty, and privacy risk, while the seller-side effective ask incorporates resource and compliance costs. This yields a resource-aware positive-margin bilateral clearing mechanism under shared resource and security constraints. To discipline post-clearing moral hazard, we further design a capped deposit-refund settlement rule based on measurable runtime compliance and update each seller's future security posture according to realized compliance outcomes. ZEBRIS satisfies bilateral individual rationality and no-subsidy weak budget balance. Experiments demonstrate that ZEBRIS improves social welfare and compliance robustness while reducing service delay and privacy-risk-weighted cost over representative baselines.