2026-09-19T08:32:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1567652024-09-17T03:37:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Virza, Madars
Narula, Neha
Ali, Ayesha
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-16T13:47:45Z
2024-09-16T13:47:45Z
2024-05
2024-07-11T14:36:48.406Z
https://hdl.handle.net/1721.1/156765
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.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Scaling Privacy Perserving Payments
Thesis
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Master
Master of Engineering in Electrical Engineering and Computer Science
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<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.</Abstract>
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