Taming Double-Spending in Offline Payments with Reputation-Weighted Loan Networks
🤖 AI Summary
To address the double-spending problem in offline payments under asynchronous network assumptions, this paper proposes Overdraft—a reputation-weighted temporal loan network that dynamically estimates payer solvency without trusted hardware. Methodologically, it introduces a novel reputation-driven dynamic loan graph model, resilient to Sybil attacks and supporting deterministic on-chain conflict resolution; adopts a “off-chain commitment + on-chain finalization” architecture; and implements offline state snapshot verification via Solidity smart contracts deployed on the Sepolia testnet. Experimental results demonstrate that Overdraft significantly enhances receivers’ quantitative assessment of transaction credibility while reducing double-spending risk—achieving a balanced trade-off among security, availability, and hardware independence—all within a decentralized framework.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Preventing double-spending in offline payments without trusted hardware
Assessing payment likelihood via reputation-weighted loan networks
Resolving transaction conflicts deterministically upon reconnecting online
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reputation-weighted loan network for offline payments
Shifts reliance from hardware to user reputation
Ethereum smart contract implementation for evaluation
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