A Review on Privacy in DAG-Based DLTs
Mayank Raikwar
TL;DR
This paper addresses the gap in privacy for DAG-based DLTs by examining privacy notions (Confidentiality, Anonymity, Unlinkability), challenges, and a taxonomy of privacy-enhancing techniques. It analyzes DAG-specific aspects such as tip selection, conflict resolution, performance, and consensus to understand how privacy mechanisms interact with DAG properties. The authors survey methods including mixing, zero-knowledge proofs, encryption, homomorphic encryption, and TEEs, highlighting their applicability, advantages, and trade-offs in DAG contexts, and outline future research directions. The work underscores the need to integrate privacy-preserving techniques without compromising the scalability and speed advantages of DAG-based DLTs, pointing to practical deployment considerations and potential hybrid approaches.
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.