Scalable Time-Lock Puzzle

TL;DR

This paper introduces a scalable, practical Time-Lock Puzzle protocol that efficiently delegates computational tasks, enabling secure, real-time verification for large-scale applications involving thousands of puzzles.

Contribution

It formally defines Delegated Time-Lock Puzzles and presents the ED-TLP protocol, the first to enable resource outsourcing and efficient processing of numerous puzzles.

Findings

ED-TLP delegates 99% of client workload

ED-TLP delegates 100% of server workload

Demonstrates efficiency with up to 10,000 puzzles

Abstract

Time-Lock Puzzles (TLPs) enable a client to lock a message such that a server can unlock it only after a specified time. They have diverse applications, such as scheduled payments, secret sharing, and zero-knowledge proofs. In this work, we present a scalable TLP designed for real-world scenarios involving a large number of puzzles, where clients or servers may lack the computational resources to handle high workloads. Our contributions are both theoretical and practical. From a theoretical standpoint, we formally define the concept of a Delegated Time-Lock Puzzle (D-TLP), establish its fundamental properties, and introduce an upper bound for TLPs, addressing a previously overlooked aspect. From a practical standpoint, we introduce the Efficient Delegated Time-Lock Puzzle (ED-TLP) protocol, which implements the D-TLP concept. This protocol enables both the client and server to securely outsource their resource-intensive tasks to third-party helpers. It enables real-time verification of solutions and guarantees their delivery within predefined time limits by integrating an upper bound and a fair payment algorithm. ED-TLP allows combining puzzles from different clients, enabling a solver to process them sequentially, significantly reducing computational resources, especially for a large number of puzzles or clients. ED-TLP is the first protocol of its kind. We have implemented ED-TLP and conducted a comprehensive analysis of its performance for up to 10,000 puzzles. The results highlight its significant efficiency in TLP applications, demonstrating that ED-TLP securely delegates 99% of the client's workload and 100% of the server's workload with minimal overhead.