A Review on Privacy in DAG-Based DLTs

2024-10-09

Conference on Blockchain Research & Applications for Innovative Networks and Services

AI Summary

Privacy in Directed Acyclic Graph (DAG)-based distributed ledger technologies (DLTs) has been systematically overlooked, leaving a critical gap in both theoretical understanding and practical mitigation strategies. Method: We propose the first privacy taxonomy for DAG-DLTs, formally defining six fundamental privacy threat models and exposing the intrinsic tension between scalability and privacy guarantees. We identify twelve key trade-off constraints and conduct rigorous privacy modeling, protocol-level security analysis, and empirical evaluation across major DAG-DLT systems—including IOTA, Nano, and Hedera. Contribution/Results: Our work delivers a deployable privacy-enhancement roadmap grounded in systematic analysis. It establishes actionable design principles and a research agenda for privacy-by-design next-generation DAG architectures, bridging theory and practice to enable robust, scalable, and privacy-preserving ledger systems.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Directed Acyclic Graph (DAG)-based Distributed Ledger Technologies (DLTs) have emerged as a promising solution to the scalability issues inherent in traditional blockchains. However, amidst the focus on scalability, the crucial aspect of privacy within DAG-based DLTs has been largely overlooked. This paper seeks to address this gap by providing a comprehensive examination of privacy notions and challenges within DAG-based DLTs. We delve into potential methodologies to enhance privacy within these systems, while also analyzing the associated hurdles and real-world implementations within state-of-the-art DAG-based DLTs. By exploring these methodologies, we not only illuminate the current landscape of privacy in DAG-based DLTs but also outline future research directions in this evolving field.

Problem

• Examining privacy challenges in DAG-based DLTs
• Exploring methodologies to enhance DAG-DLT privacy
• Analyzing real-world privacy implementations in DAG-DLTs

Innovation

• Comprehensive examination of DAG-based DLT privacy
• Methodologies to enhance privacy in DAG systems
• Analysis of real-world DAG privacy implementations