A Segmented PageRank-Based Value Compensation Method for Personal Data in Alliance Blockchains

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2022-11-28 DOI:10.1016/j.bdr.2022.100326

Chaoxia Qin , Bing Guo , Yun Zhang , Omar Cheikhrouhou , Yan Shen , Zhen Zhang , Hong Su

{"title":"A Segmented PageRank-Based Value Compensation Method for Personal Data in Alliance Blockchains","authors":"Chaoxia Qin , Bing Guo , Yun Zhang , Omar Cheikhrouhou , Yan Shen , Zhen Zhang , Hong Su","doi":"10.1016/j.bdr.2022.100326","DOIUrl":null,"url":null,"abstract":"<div>Alliance blockchains provide a multi-party trusted data trading environment, promoting the development of the data trading market in which the value compensation for personal data is still a key issue. However, limited by the data format and content, traditional attempts on data value compensation cannot form a widely applicable solution. Therefore, we propose a universal value compensation method for personal data in alliance blockchains. The basic idea of this method is to evaluate the value weight of data based on the collaborative relationship of data value. First, we construct a Data Collaboration Markov Model (DCMM) to formalize the collaboration network of data value. Then, aiming at data collaboration networks with different structures, the corresponding Segmented PageRank (SPR) algorithm is proposed. SPR can universally evaluate the value weight of each data account without being subjected to the data format or content. Finally, we theoretically deduce that the time complexity and space complexity of SPR algorithm are respectively <math><mn>1</mn><mo>/</mo><mi>K</mi></math> and <math><mn>1</mn><mo>/</mo><msup><mrow><mi>K</mi></mrow><mrow><mn>2</mn></mrow></msup></math> taken by PageRank algorithm. Experiments show the feasibility and superior performance of SPR.</div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100326"},"PeriodicalIF":4.2000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221457962200020X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 2

Abstract

Alliance blockchains provide a multi-party trusted data trading environment, promoting the development of the data trading market in which the value compensation for personal data is still a key issue. However, limited by the data format and content, traditional attempts on data value compensation cannot form a widely applicable solution. Therefore, we propose a universal value compensation method for personal data in alliance blockchains. The basic idea of this method is to evaluate the value weight of data based on the collaborative relationship of data value. First, we construct a Data Collaboration Markov Model (DCMM) to formalize the collaboration network of data value. Then, aiming at data collaboration networks with different structures, the corresponding Segmented PageRank (SPR) algorithm is proposed. SPR can universally evaluate the value weight of each data account without being subjected to the data format or content. Finally, we theoretically deduce that the time complexity and space complexity of SPR algorithm are respectively $1 / K$ and $1 / K^{2}$ taken by PageRank algorithm. Experiments show the feasibility and superior performance of SPR.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

联盟区块链中基于分段pagerank的个人数据价值补偿方法

联盟区块链提供了多方可信的数据交易环境，促进了个人数据价值补偿仍是关键问题的数据交易市场的发展。然而，由于数据格式和内容的限制，传统的数据价值补偿尝试无法形成一种广泛适用的解决方案。因此，我们提出了一种联盟区块链中个人数据的通用价值补偿方法。该方法的基本思想是基于数据价值的协同关系来评估数据的价值权重。首先，构建数据协作马尔可夫模型(DCMM)来形式化数据价值的协作网络。然后，针对不同结构的数据协作网络，提出了相应的分段PageRank (SPR)算法。SPR可以在不受数据格式和内容限制的情况下，统一评价每个数据账户的价值权重。最后，我们从理论上推导出SPR算法的时间复杂度和空间复杂度分别为PageRank算法的1/K和1/K2。实验证明了SPR的可行性和优越的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

期刊介绍： The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.

期刊最新文献

Realistic image-to-image machine unlearning via decoupling and knowledge retention Explainable classification of astronomical uncertain time series Opinion fraud detection on massive datasets by spark Large-scale least squares regression based on fast spectral embedding and random Fourier feature mapping VertexLocater: PIM-enabled dynamic offloading for graph computing