负责任的分散ttp和激励机制的元宇宙数据交换

IF 5.7 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Liang Zhang;Xingyu Wu;Yuhang Ma;Haibin Kan
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引用次数: 0

摘要

作为一个全球性的虚拟环境,元宇宙在数据存储、共享、互操作性和隐私保护方面提出了各种挑战。通常,在这些场景中,可信第三方(TTP)被认为是必要的。但是,依赖于单个http可能会引入偏差、损害隐私或导致单点故障问题。为了应对这些挑战并实现虚拟世界中的安全数据交换,我们提出了一个基于去中心化https和以太坊区块链的系统。首先,我们使用阈值ElGamal密码系统创建去中心化的https,采用可验证的秘密共享(VSS)来强制所有者诚实地共享数据。其次,我们利用以太坊区块链作为公共通信通道、自动验证机和智能合约引擎。第三,我们应用离散对数等式(DLEQ)算法在加密数据上传到区块链时生成非交互式零知识(NIZK)证明。第四,我们提出了一种激励机制,使数据所有者和ttp从数据共享活动中受益,以及如果检测到恶意行为的惩罚政策。因此,我们为元世界构建了一个数据交换框架,其中所有涉及的实体都是负责任的。最后,我们进行了全面的实验来证明该系统的可行性,并分析了该系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Exchange for the Metaverse With Accountable Decentralized TTPs and Incentive Mechanisms
As a global virtual environment, the metaverse poses various challenges regarding data storage, sharing, interoperability, and privacy preservation. Typically, a trusted third party (TTP) is considered necessary in these scenarios. However, relying on a single TTP may introduce biases, compromise privacy, or lead to single-point-of-failure problem. To address these challenges and enable secure data exchange in the metaverse, we propose a system based on decentralized TTPs and the Ethereum blockchain. First, we use the threshold ElGamal cryptosystem to create the decentralized TTPs, employing verifiable secret sharing (VSS) to force owners to share data honestly. Second, we leverage the Ethereum blockchain to serve as the public communication channel, automatic verification machine, and smart contract engine. Third, we apply discrete logarithm equality (DLEQ) algorithms to generate non-interactive zero knowledge (NIZK) proofs when encrypted data is uploaded to the blockchain. Fourth, we present an incentive mechanism to benefit data owners and TTPs from data-sharing activities, as well as a penalty policy if malicious behavior is detected. Consequently, we construct a data exchange framework for the metaverse, in which all involved entities are accountable. Finally, we perform comprehensive experiments to demonstrate the feasibility and analyze the properties of the proposed system.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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