On Fair Ordering and Differential Privacy

TL;DR

This paper explores fairness in blockchain transaction ordering, linking it to differential privacy, and proposes methods to eliminate bias by ensuring order depends only on relevant features.

Contribution

It introduces a fairness framework based on equal opportunity, connecting transaction ordering fairness with differential privacy mechanisms in blockchain systems.

Findings

Fair transaction ordering can be achieved using differential privacy techniques.

A formal link between equal opportunity and differential privacy is established.

The approach helps eliminate algorithmic bias in blockchain transaction ordering.

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.