Blockchain Privacy Through Merge Avoidance and Mixing Services: a Hardness and an Impossibility Result
This work establishes fundamental limits for privacy mechanisms in blockchain-based cryptocurrencies, which is important for users and developers aiming to enhance privacy.
This paper investigates two privacy-enhancing strategies for blockchain-based cryptocurrencies: merge avoidance and mixing services. It demonstrates that optimal merge avoidance is an NP-hard optimization problem, and incentive-compatible mixing services face a class of impossibility results.
Cryptocurrencies typically aim at preserving the privacy of their users. Different cryptocurrencies preserve privacy at various levels, some of them requiring users to rely on strategies to raise the privacy level to their needs. Among those strategies, we focus on two of them: merge avoidance and mixing services. Such strategies may be adopted on top of virtually any blockchain-based cryptocurrency. In this paper, we show that whereas optimal merge avoidance leads to an NP-hard optimization problem, incentive-compatible mixing services are subject to a certain class of impossibility results. Together, our results contribute to the body of work on fundamental limits of privacy mechanisms in blockchain-based cryptocurrencies.