Proof of User Similarity: the Spatial Measurer of Blockchain

TL;DR

This paper introduces Proof of User Similarity (PoUS), an energy-efficient blockchain consensus mechanism that leverages user similarity calculations to improve performance and enable spatial analysis, with promising experimental results.

Contribution

PoUS is a novel energy-recycling consensus mechanism that uses user similarity computations to enhance blockchain performance and spatial analysis capabilities.

Findings

PoUS achieves 24.01% higher TPS than PoW.

PoUS reduces confirmation latency by 43.64%.

PoUS effectively mirrors user spatial information with minimal overhead.

Abstract

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism, harnessing the valuable computing power to calculate the similarities of users, and enact the calculation results into the packing rule. However, the expensive calculation required in PoUS challenges miners in participating, and may induce plagiarism and lying risks. To resolve these issues, PoUS embraces the best-effort schema by allowing miners to compute partially. Besides, a voting mechanism based on the two-parties computation and Bayesian truth serum is proposed to guarantee privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes itself in recycling the computing power back to blockchain since it turns the resource wastage to facilitate refined cohort analysis of users, serving as the spatial measurer and enabling a searchable blockchain. We build a prototype of PoUS and compare its performance with PoW. The results show that PoUS outperforms PoW in achieving an average TPS improvement of 24.01% and an average confirmation latency reduction of 43.64%. Besides, PoUS functions well in mirroring the spatial information of users, with negligible computation time and communication cost.