Taming Double-Spending in Offline Payments with Reputation-Weighted Loan Networks

TL;DR

This work addresses the challenge of double-spending in offline payments by removing reliance on trusted hardware and introducing a reputation-weighted loan network that underpins offline guarantees. It combines MeritRank-based reputation with time-bound, signed loans recorded on a blockchain, enabling offline participants to estimate the probability of eventual payment and proceed accordingly. The authors implement an on-chain smart contract for loan management and offline-offline reconciliation, supported by an offline Python component and an incentive model that ties loan terms to lender reputation via a sigmoid-based interest formula. Through a Sepolia-based prototype and extensive simulations, they demonstrate Sybil-tolerance mechanisms, practical runtime for the confidence evaluation, and a cost profile that can be mitigated with bundling and L2 solutions, highlighting the method's potential for secure, hardware-free offline transactions with real-world applicability.

Abstract

Blockchain solutions typically assume a synchronous network to ensure consistency and achieve consensus. In contrast, offline transaction systems aim to enable users to agree on and execute transactions without assuming bounded communication delays when interacting with the blockchain. Most existing offline payment schemes depend on trusted hardware wallets that are assumed to be secure and tamper-proof. While this work introduces Overdraft, a novel offline payment system that shifts the reliance from hardware to users themselves. Overdraft allows potential payment receivers to assess the likelihood of being paid, allowing them to accept transactions with confidence or deny them. Overdraft achieves this by maintaining a loan network that is weighted by online reputation. This loan network contains time-limited agreements where users pledge to cover another user's payment if necessary. For example, when a payer lacks sufficient funds at the moment of commitment. Offline users rely on the last known view of the loan network -- which they had access to when last online -- to determine whether to participate in an offline transaction. This view is used to estimate the probability of eventual payment, possibly using multiple loans. Once online again, users commit their transactions to the blockchain with any conflicts being resolved deterministically. Overdraft incorporates incentives for users and is designed to be resilient against Sybil attacks. As a proof of concept, we implemented Overdraft as an Ethereum Solidity smart contract and deployed it on the Sepolia testnet to evaluate its performance.

Figures (7)

• Figure 1: Overview of Overdraft
• Figure 2: A simplified loan network that can be used to simulate the online processing of an offline payment by user 1. A cycle, such as the one created by users 1 and 2 loaning 10 coins to each other, might lead to infinite recursion during the randomized exploration if edges could be visited multiple times.
• Figure 2: Time (ms) necessary to retrieve the probability distribution regarding the maximum retrievable loan amount, comparing results without optimizations (Wo. Opt.) and with optimizations (With Opt.).
• Figure 3: Total interest earned depending on reputation and loan amount.
• Figure 4: Sybil attacks exploiting loan agreements in the network.