Scaling Privacy Perserving Payments
Name
ali-ayesha23-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
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1.97 MB
Format
Adobe PDF
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Author(s)
Ali, Ayesha
Advisor(s)
Virza, Madars
Narula, Neha
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
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 tree-based 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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