ZkRep: A Privacy-Preserving Scheme for Reputation-Based Blockchain System

Reputation/trust-based blockchain systems have attracted considerable research interests for better integrating Internet of Things with blockchain in terms of throughput, scalability, energy efficiency, and incentive aspects. However, most existing works only consider static adversaries. Hence, they are vulnerable to slowly adaptive attackers, who can target validators with high reputation value to severely degrade the system performance. Therefore, we introduce $\textsf{zkRep}$ , a privacy-preserving scheme tailored for reputation-based blockchains. Our basic idea is to hide both the identity and reputation of the validators by periodically changing the identity and reputation commitments (i.e., aliases), which makes it much more difficult for slowly adaptive attackers to identify validators with high reputation value. To realize this idea, we utilize privacy-preserving Pedersen-commitment-based reputation updating and leader election schemes that operate on concealed reputations within an epoch. We also introduce a privacy-preserving identity update protocol that changes the identity and time-window-based cumulative reputation commitments during each epoch transition. We have implemented and evaluated $\textsf{zkRep}$ on the Amazon Web Service. The experimental results and analysis show that $\textsf{zkRep}$ achieves great privacy-preserving features against slowly adaptive attacks with little overhead.

I. Introduction

Currently, the integration of Internet of Things (IoT) and blockchain shows the great potential to overcome many security issues, such as point of failure, DDoS, Sybil attack and, etc. [1]–[3]. However, blockchain suffers from throughput, scalability, energy consumption, and incentive issues. Thus, the reputation/trust concept has attracted considerable interest in the combination of blockchain and IoT research community [3], [4]. The core idea of reputation is to fully consider the heterogeneity property of the IoT devices, i.e., to quantify each validators’ behavior/contribution so as to provide a basis for system improvement [3]–[6]. Such behavior could be the validator’s contribution of block generation [3], [5]; or the participation in the Byzantine fault tolerance (BFT) voting procedure [4]; or the trust link between each other [6]. Recent works have successfully leveraged reputation to enhance the throughput and scalability of BFT-based blockchains [4], [5] and reduce the energy costs of Proof-of-Work (PoW) systems [3], [6]. Moreover, introducing reputation makes it smoother to integrate with incentive schemes [4], [6].