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RegGuard: Legitimacy and Fairness Enforcement for Optimistic Rollups

Zhenhang Shang, Yingzhe Yu, Kani Chen

Abstract

Optimistic rollups provide scalable smart-contract execution but remain unsuitable for regulated financial applications due to three structural gaps: semantic legitimacy, cross-layer state consistency, and ordering fairness. We introduce RegGuard, a unified framework that enhances optimistic rollups with comprehensive legitimacy guarantees. RegGuard integrates three coordinated mechanisms: a decidable semantic validator powered by the RegSpec rule language for encoding regulatory constraints; a cross-layer state pre-synchronization validator that detects inconsistent L1-L2 dependencies with probabilistic reliability bounds; and a cryptographically verifiable fair-ordering service that ensures transaction sequencing fairness with negligible violation probability. We implement a 15,000-line prototype integrated into an Optimism-based rollup and evaluate it under adversarial conditions. RegGuard reduces settlement failures by over 90%, prevents detectable ordering manipulation, and maintains 85% of baseline throughput.

RegGuard: Legitimacy and Fairness Enforcement for Optimistic Rollups

Abstract

Optimistic rollups provide scalable smart-contract execution but remain unsuitable for regulated financial applications due to three structural gaps: semantic legitimacy, cross-layer state consistency, and ordering fairness. We introduce RegGuard, a unified framework that enhances optimistic rollups with comprehensive legitimacy guarantees. RegGuard integrates three coordinated mechanisms: a decidable semantic validator powered by the RegSpec rule language for encoding regulatory constraints; a cross-layer state pre-synchronization validator that detects inconsistent L1-L2 dependencies with probabilistic reliability bounds; and a cryptographically verifiable fair-ordering service that ensures transaction sequencing fairness with negligible violation probability. We implement a 15,000-line prototype integrated into an Optimism-based rollup and evaluate it under adversarial conditions. RegGuard reduces settlement failures by over 90%, prevents detectable ordering manipulation, and maintains 85% of baseline throughput.

Paper Structure

This paper contains 34 sections, 3 theorems, 23 equations, 5 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Rollup correctness and semantic legitimacy
  4. MEV mitigation and fair ordering
  5. Cross-layer consistency and blockchain compliance
  6. System Model and Problem Formulation
  7. Transaction and State Transition Model
  8. Semantic Legitimacy
  9. Cross-Layer Consistency
  10. Fair Ordering
  11. Problem Summary
  12. Threat Model
  13. Malicious sequencer.
  14. Malicious users.
  15. Byzantine L1 oracle.
  16. ...and 19 more sections

Key Result

Theorem 1

Let $R$ be a RegSpec rule set restricted to the decidable fragment of first-order logic with linear arithmetic and finite map accesses. For any transaction $tx$ and L2 state $S$, the semantic validator $V_{\mathrm{sem}}(tx, S)$ halts and correctly decides semantic legitimacy. Moreover, if $L$ denote where $\mathcal{R}_f$ is the subset of rules applicable to the target function of $tx$. $\blacktria

Figures (5)

  • Figure 1: RegGuard system architecture. Transactions flow through sequential validation stages: semantic validation using RegSpec rules, cross-layer state consistency checking via L1 state cache, and fair ordering through threshold encryption. Legitimate transactions proceed to execution while violations are rejected early. The L1 blockchain provides final settlement and security guarantees.
  • Figure 2: Semantic validator performance showing throughput degradation as RegSpec rule complexity increases. Simple transactions maintain 88% of baseline throughput even with 20 rules, while complex transactions show 76% throughput, demonstrating the validator's efficiency in handling regulatory constraints.
  • Figure 3: State conflict reduction effectiveness across different application scenarios. RegGuard reduces settlement failures by 85-96% compared to baseline, with the most significant improvement in KYC status verification (96.3% reduction). Error bars represent 95% confidence intervals across 1000 experimental runs.
  • Figure 4: Fair ordering service maintains strong security guarantees under adversarial conditions. The system preserves $\beta < 10^-4$ fairness violation probability even with 49% malicious sequencers, while throughput remains above 88% of baseline. Dual $y$-axes show the trade-off between security ($\beta$) and performance (throughput).
  • Figure 5: End-to-end system resource distribution across RegGuard components. The fair ordering service contributes the largest latency overhead (40ms), while CPU and memory usage are distributed across all components. Total system overhead remains within practical deployment limits for production environments.

Theorems & Definitions (11)

  • Definition 1: Semantic Legitimacy
  • Definition 2: Cross-Layer State Conflict
  • Definition 3: $(\alpha,\beta)$-Fair Ordering
  • Theorem 1: Decidability of Semantic Validation
  • proof
  • Remark 1: Example: Transfer Eligibility Rule
  • Remark 2: Example: Multi-Rule AML Compliance
  • Theorem 2: Reliability of State Pre-synchronization
  • proof
  • Theorem 3: $(\alpha,\beta)$-Fairness of Ordering Service
  • ...and 1 more