Confidential Wrapped Ethereum
- URL: http://arxiv.org/abs/2507.09231v1
- Date: Sat, 12 Jul 2025 10:00:50 GMT
- Title: Confidential Wrapped Ethereum
- Authors: Artem Chystiakov, Mariia Zhvanko,
- Abstract summary: The proposal suggests creating a confidential version of wrapped (cWETH) fully within the application layer.<n>The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality.<n>To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Transparency is one of the key benefits of public blockchains. However, the public visibility of transactions potentially compromises users' privacy. The fundamental challenge is to balance the intrinsic benefits of blockchain openness with the vital need for individual confidentiality. The proposal suggests creating a confidential version of wrapped Ethereum (cWETH) fully within the application layer. The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality and the EC Diffie-Hellman (DH) protocol to introduce accessibility limited by the commitment scheme. To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.
Related papers
- Novel Blockchain-based Protocols for Electronic Voting and Auctions [0.0]
We consider several decentralized protocols to be built on blockchains, specifically using smart contracts on smart contracts.<n>We proposed a new approach called Blind Vote, which is untraceable, secure, efficient, secrecy-preserving, and fully on-chain electronic voting protocol.<n>On the other hand, we propose a new family of algorithms for private, trustless auctions that protect bidder identities and bid values.
arXiv Detail & Related papers (2025-07-04T02:26:04Z) - Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain [39.093148638790346]
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments.<n>Emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets.<n>However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments.
arXiv Detail & Related papers (2025-06-11T04:32:54Z) - Privacy-Preserving Smart Contracts for Permissioned Blockchains: A zk-SNARK-Based Recipe Part-1 [1.7265013728931]
This work proposes a solution utilizing zk-SNARKs to provide privacy in smart contracts and blockchains.<n>The proposal includes a new type of transactions, called delegated transactions, which enable use cases like Delivery vs Payment (DvP)
arXiv Detail & Related papers (2025-01-06T21:16:33Z) - Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [46.404531555921906]
We propose an architecture for blockchain-based PAISs aimed at preserving both confidentiality and transparency.<n>Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.
arXiv Detail & Related papers (2024-12-07T20:18:36Z) - The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols.<n>Our measurements from the Aptos mainnet show that the optimistic approach reduces latency overhead by 71%.
arXiv Detail & Related papers (2024-07-16T20:53:04Z) - MARTSIA: A Tool for Confidential Data Exchange via Public Blockchain [6.26635837045368]
Multi-Authority Approach to Transaction Systems for Interoperating Applications (MARTSIA)<n>MARTSIA provides fine-grained read-access control at the message-part level by combining user-defined policies with certifier-declared attributes.<n>This architecture effectively balances the transparency inherent in public blockchains with the privacy required for sensitive applications.
arXiv Detail & Related papers (2024-07-15T12:59:54Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - HE-DKSAP: Privacy-Preserving Stealth Address Protocol via Additively Homomorphic Encryption [15.902511928891643]
Homomorphic Encryption-based Dual-Key Stealth Address Protocol (HE-DKSAP)
This paper delves into the core principles of HE-DKSAP, highlighting its capacity to enhance privacy, scalability, and security in programmable blockchains.
arXiv Detail & Related papers (2023-12-17T12:23:49Z) - SeDe: Balancing Blockchain Privacy and Regulatory Compliance by Selective De-Anonymization [0.46040036610482665]
We propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe)<n>Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions.
arXiv Detail & Related papers (2023-11-14T13:49:13Z) - An Efficient and Multi-private Key Secure Aggregation for Federated Learning [41.29971745967693]
We propose an efficient and multi-private key secure aggregation scheme for federated learning.
Specifically, we skillfully modify the variant ElGamal encryption technique to achieve homomorphic addition operation.
For the high dimensional deep model parameter, we introduce a super-increasing sequence to compress multi-dimensional data into 1-D.
arXiv Detail & Related papers (2023-06-15T09:05:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.