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Xproofs: New Aggregatable and Maintainable Matrix Commitment with Optimal Proof Size
Xinwei Yong, Jiaojiao Wu, Jianfeng Wang

Xproofs: New Aggregatable and Maintainable Matrix Commitment with Optimal Proof Size

Science & Mathematics Published 2024 Verification Pending

Overview & Synopsis

Vector Commitment (VC) enables one to commit to a vector, and then the element at a specific position can be opened, with proof of consistency to the initial commitment. VC is a powerful primitive with various applications, including stateless cryptocurrencies. Recently, matrix commitment Matproofs (Liu and Zhang CCS 2022), as an extension of VC, has been proposed to reduce the communication and computation complexity of VC-based cryptocurrencies. However, Matproofs requires linear-sized public parameters, and the aggregated proof size may also increase linearly with the number of individual proofs aggregated. Additionally, the proof updating process involves a third party, known as Proof-Serving Nodes (PSNs), which leads to extra storage and communication overhead. In this paper, we first propose a multi-dimensional variant of matrix commitment and construct a new matrix commitment scheme for two-dimensional matrix, called 2D-Xproofs, which achieves optimal aggregated proof size without using PSNs. Furthermore, we present a highly maintainable three-dimensional scheme, 3D-Xproofs, which updates all proofs within sublinear time in the size of the committed matrix without PSNs’ assistance. More generally, we could further increase the matrix dimensionality to achieve more efficient proof updates. Finally, we demonstrate the security of our schemes, showing that both schemes are position binding. We also implement both schemes, and the results indicate that our schemes enjoy constant-sized aggregated proofs and sublinear-sized public parameters, and the proof update time in 3D-Xproofs is 2.5$$\times $$× faster than Matproofs.

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Document Information

Document Title Xproofs: New Aggregatable and Maintainable Matrix Commitment with Optimal Proof Size
Author / Organization Xinwei Yong, Jiaojiao Wu, Jianfeng Wang
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2024
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-47257
Original Source Link Visit Source Page on hal.science »