Ali, A. (2024) Scaling Privacy Preserving Payments. [Master's thesis, MIT]. https://www.mit.edu/publications/scaling-privacy-preserving-payments

Abstract

We explore privacy-preserving payments in a centralized setting, such as CBDCs. Specifically, we focus on two classes of designs that hide the transaction graph: Chaumian e-cash and Merkle tree-based systems (e.g., Tornado Cash), which differ both in their security assumptions and scalability. In our work we highlight scalability limitations in Merkle treebased privacy systems that would be encountered in a network as large as a CBDC, and propose a sharded Merkle tree design to improve scalability while maintaining strong privacy. However, as we analyze, conventional sharding methods pose privacy risks, prompting introduction of a ’tree of sharded trees’ design that preserves privacy at a modest increase of latency. We describe, implement and evaluate all three designs, and find that unmodified Tornado Cash indeed suffers from resource-contention induced scalability bottlenecks. In contrast, our new design is achieves throughput that is less than an order of magnitude away from e-cash, despite providing auditability. 

Thesis supervisor: Madars Virza
Title: Research Scientist, Digital Currency Initiative 

Thesis supervisor: Neha Narula
Title: Director, Digital Currency Initiative