A Systematic Review and Taxonomy of Privacy-Preserving Blockchain Consensus Mechanisms

Blockchain systems rely on consensus mechanisms to validate transactions and coordinate distributed participants, making consensus a critical layer that shapes security, trust, and privacy. Although blockchain is increasingly applied in privacy-sensitive domains such as healthcare, smart cities, and the Internet of Things, existing review studies primarily examine security or performance and rarely analyse how consensus-level design properties influence privacy risks. As a result, privacy is often treated as a peripheral enhancement rather than a core consensus concern. This study presents a systematic literature review that examines blockchain consensus mechanisms from a privacy-focused perspective. The review aims to identify which consensus classes are most commonly used in privacy-preserving blockchain systems, what privacy limitations are reported across different consensus designs, and how privacy-preserving techniques are integrated into consensus mechanisms. The review follows PRISMA and Kitchenham-guided procedures, using structured search and screening of peer-reviewed journal articles from major academic databases, followed by relevance and quality assessment. 72 peer-reviewed journal articles were synthesised using taxonomy-based and thematic analysis. The proposed taxonomy explicitly classifies studies by consensus mechanism class, privacy limitation, and integration level, enabling structured comparison beyond existing surveys. The findings show that Byzantine Fault Tolerant (BFT)-based consensus mechanisms are most frequently adopted in privacy-preserving blockchain applications. However, privacy challenges such as identity exposure and communication pattern leakage remain common and are closely linked to consensus design properties. In addition, most studies rely on external privacy mechanisms rather than embedding privacy directly into the consensus layer. This review contributes a structured taxonomy, clear analytical insights, and practical guidance that support the development and evaluation of privacy-aware blockchain consensus mechanisms.