Bayesi-Chain: Intelligent Identity Authentication

Juanita Blue, J. Condell, T. Lunney, Eoghan Furey
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引用次数: 6

Abstract

In a bid to stamp out fraudulent crime, there is increased pressure on individuals to provide evidence that they possess a ‘real’ identity. Counterfeiting and fake identities have reduced confidence in traditional paper documentation as proof of identity, this has created a demand for an intelligent digital alternative. Recent government implementations and identity trends have also improved the popularity of digital forms of identification.Authentication of identity is a salient issue in the current climate where identity theft through record duplication is on the rise. Identity resolution techniques have proven effective in filtering duplicated and fake records in identity management systems. These techniques have been further improved by the implementation of machine learning techniques which are capable of revealing patterns and links that have formerly gone undetected. Research has also suggested that incorporating non-standard attributes in the form of social contextual data can increase the efficiency and success-rate of these fraud detection methods.In the digital age where individuals are creating large digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to ‘proof’ that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity.This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques. Where identity sources are considered reliable, a secure and intelligent digital identification artefact will be created. This artefact will emulate a blockchain-inspired ledger and may subsequently be used to prove identity in place of traditional paper documentation.
贝叶斯链:智能身份认证
为了杜绝欺诈性犯罪,政府对个人施加了越来越大的压力,要求他们提供拥有“真实”身份的证据。伪造和伪造身份降低了人们对传统纸质文件作为身份证明的信心,这就产生了对智能数字替代品的需求。最近的政府实施和身份趋势也提高了数字身份形式的普及程度。身份认证是一个突出的问题,在当前的气候下,通过记录复制身份盗窃正在上升。在身份管理系统中,身份解析技术在过滤重复和虚假记录方面已被证明是有效的。通过实现机器学习技术,这些技术得到了进一步的改进,机器学习技术能够揭示以前未被发现的模式和链接。研究还表明,以社会背景数据的形式纳入非标准属性可以提高这些欺诈检测方法的效率和成功率。在数字时代,个人正在创造大量的数字足迹,在线账户和活动可以被证明是一个有价值的信息来源,可能有助于“证明”所断言的身份是真实的。在线社交背景数据——或“数字身份”——与真实的人有关,是随着时间的推移而建立起来的,并得到相关账户、关系和属性的支持。这些数据很难伪造,因此可能有能力提供“真实”身份的证明。本文概述了一种解决方案的设计和初步开发,该解决方案利用来自个人数字足迹的数据来评估它属于“真实”身份的可能性。这是通过应用机器学习和贝叶斯概率建模技术来实现的。在身份来源被认为是可靠的地方,将创建一个安全和智能的数字识别人工制品。这个人工制品将模拟一个受区块链启发的分类账,随后可能被用来证明身份,取代传统的纸质文件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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