On Fair Ordering and Differential Privacy

2025-01-09

AI Summary

This paper addresses the joint challenge of fairness and privacy preservation in blockchain transaction ordering. Method: It introduces a novel definition of ordering fairness based on relevance features—transactions sharing identical relevance features must exhibit equal relative ordering probabilities. It establishes, for the first time, a theoretical equivalence between ordering fairness in state-machine replication and differential privacy, proving that any differentially private ordering mechanism inherently satisfies this fairness criterion. The paper then translates “equal opportunity” into verifiable ordering constraints and designs a distance-sensitive probabilistic priority mechanism. Contribution/Results: Through formal modeling, feature decoupling, and protocol analysis, it provides a new paradigm and practical construction pathway for distributed consensus protocols that are provably fair and privacy-secure.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In blockchain systems, fair transaction ordering is crucial for a trusted and regulation-compliant economic ecosystem. Unlike traditional State Machine Replication (SMR) systems, which focus solely on liveness and safety, blockchain systems also require a fairness property. This paper examines these properties and aims to eliminate algorithmic bias in transaction ordering services. We build on the notion of equal opportunity. We characterize transactions in terms of relevant and irrelevant features, requiring that the order be determined solely by the relevant ones. Specifically, transactions with identical relevant features should have an equal chance of being ordered before one another. We extend this framework to define a property where the greater the distance in relevant features between transactions, the higher the probability of prioritizing one over the other. We reveal a surprising link between equal opportunity in SMR and Differential Privacy (DP), showing that any DP mechanism can be used to ensure fairness in SMR. This connection not only enhances our understanding of the interplay between privacy and fairness in distributed computing but also opens up new opportunities for designing fair distributed protocols using well-established DP techniques.

Problem

• Blockchain
• Transaction Ordering Fairness
• User Privacy Protection

Innovation

• Opportunity Equality
• Differential Privacy
• Fair Protocol Design