利用区块链技术实现大数据市场数据的可追溯性和所有权主张

IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Information and Telecommunication Pub Date : 2020-09-29 DOI:10.1080/24751839.2020.1819634
Swagatika Sahoo, Raju Halder
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引用次数: 13

摘要

在大数据时代,现代数据市场不仅允许大型企业进行数据交易,也允许个人进行数据交易,因此备受关注。这种新模式使数据容易受到各种威胁,包括盗版、非法转售、篡改、非法再分发、所有权索赔、伪造、盗窃、盗用等。虽然数字水印是解决上述挑战的一种很有前途的技术,但由于以下因素,现有文献中的解决方案在大数据场景中被认为是不胜任的:大数据的V,多个所有者的参与,增量水印,大覆盖尺寸和有限的水印容量,不干扰等。在本文中,我们提出了一种新的大数据水印技术,该技术利用区块链技术的力量,为大数据货币化场景中的数据移动提供透明的不可变审计跟踪。在这方面,我们应对上述所有关键挑战。我们在以太坊平台上使用Solidity提出了该系统的原型实现作为概念验证,并进行了实验评估,以证明其在执行gas成本方面的可行性和有效性。据我们所知,这是第一个在大数据背景下处理水印问题的提案。
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Traceability and ownership claim of data on big data marketplace using blockchain technology
ABSTRACT In the era of big data, modern data marketplaces have received much attention as they allow not only large enterprises but also individuals to trade their data. This new paradigm makes the data prone to various threats, including piracy, illegal reselling, tampering, illegal redistribution, ownership claiming, forgery, theft, misappropriation, etc. Although digital watermarking is a promising technique to address the above-mentioned challenges, the existing solutions in the literature are deemed to be incompetent in big data scenarios due to the following factors: V's of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. In this paper, we propose a novel big data watermarking technique that leverages the power of blockchain technology and provides a transparent immutable audit trail for data movement in big data monetizing scenarios. In this context, we address all the crucial challenges mentioned above. We present a prototype implementation of the system as a proof of concept using Solidity on Ethereum platform, and we perform experimental evaluation to demonstrate its feasibility and effectiveness in terms of execution gas costs. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.
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来源期刊
CiteScore
7.50
自引率
0.00%
发文量
18
审稿时长
27 weeks
期刊最新文献
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