DEBPIR:增强分散业务建模中的信息隐私

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gulshan Kumar, Rahul Saha, Mauro Conti, Tai Hoon Kim
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引用次数: 0

摘要

商业建模通常涉及广泛的数据收集和分析,这引发了人们对侵犯隐私的担忧。在业务模型中集成隐私信息检索(PIR)机制对于解决隐私问题、确保遵守法规、保护敏感数据和维护利益相关者的信任至关重要;然而,PIR尚未包含在现有的业务模型中。在本文中,我们提出了第一个使用PIR的分散业务模型。我们将我们提出的模型称为带有PIR的分散式业务模型(DEBPIR)。DEBPIR使用PIR和加密的智能合约来共享机密信息的隐私。我们在DEBPIR上执行了一组全面的实验,并根据隐私实现、延迟和吞吐量评估结果。我们还对我们提出的DEBPIR模型与现有模型进行了比较分析;我们观察到DEBPIR优于现有模型,并提供\(95\%\)隐私实现。与现有模型相比,我们提出的DEBPIR的延迟和吞吐量不会超出。因此,DEBPIR是业务模型的有效解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEBPIR: enhancing information privacy in decentralized business modeling

Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. Integrating Privacy Information Retrieval (PIR) mechanisms within business models is crucial to address privacy concerns, ensure compliance with regulations, safeguard sensitive data, and maintain trust with stakeholders; however, PIR is not included in the existing business models yet. In this paper, we propose the first decentralized business model that uses the PIR. We call our proposed model DEcentralized Business model with PIR (DEBPIR). DEBPIR uses a smart contract for PIR and encryption to share the privacy of classified information. We execute a thorough set of experiments on DEBPIR and evaluate the results based on privacy attainment, latency, and throughput. We also perform a comparative analysis between our proposed DEBPIR and the existing models; we observe that DEBPIR outperforms the existing models and provides \(95\%\) privacy attainment. The latency and throughput of our proposed DEBPIR do not outgrow compared to the existing models. Thus, DEBPIR is an efficient solution for business models.

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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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