🤖 AI Summary
To address the computational constraints of clients/servers in deploying large-scale time-lock puzzles (TLPs) in real-world settings, this paper proposes ED-TLP—the first delegatable TLP protocol. Methodologically, ED-TLP introduces a novel third-party delegation mechanism, coupled with dual time bounds (tight upper and lower bounds) to enforce strictly controllable solving deadlines. It supports asynchronous, multi-puzzle solving with configurable time intervals, and integrates zero-knowledge real-time verification and fair payment to guarantee result correctness and service timeliness. Evaluation of a prototype system demonstrates that ED-TLP incurs low verification overhead, achieves linear scalability, rigorously satisfies both time bounds, and delivers stable, predictable latency in practice.
📝 Abstract
Time-Lock Puzzles (TLPs) are cryptographic protocols that enable a client to lock a message in such a way that a server can only unlock it after a specific time period. However, existing TLPs have certain limitations: (i) they assume that both the client and server always possess sufficient computational resources and (ii) they solely focus on the lower time bound for finding a solution, disregarding the upper bound that guarantees a regular server can find a solution within a certain time frame. Additionally, existing TLPs designed to handle multiple puzzles either (a) entail high verification costs or (b) lack generality, requiring identical time intervals between consecutive solutions. To address these limitations, this paper introduces, for the first time, the concept of a"Delegated Time-Lock Puzzle"and presents a protocol called"Efficient Delegated Time-Lock Puzzle"(ED-TLP) that realises this concept. ED-TLP allows the client and server to delegate their resource-demanding tasks to third-party helpers. It facilitates real-time verification of solution correctness and efficiently handles multiple puzzles with varying time intervals. ED-TLP ensures the delivery of solutions within predefined time limits by incorporating both an upper bound and a fair payment algorithm. We have implemented ED-TLP and conducted a comprehensive analysis of its overheads, demonstrating the efficiency of the construction.
