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USign--a security enhanced electronic consent model.

作者信息

Li Yanyan, Xie Mengjun, Bian Jiang

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4487-90. doi: 10.1109/EMBC.2014.6944620.

DOI:10.1109/EMBC.2014.6944620
PMID:25570988
Abstract

Electronic consent becomes increasingly popular in the healthcare sector given the many benefits it provides. However, security concerns, e.g., how to verify the identity of a person who is remotely accessing the electronic consent system in a secure and user-friendly manner, also arise along with the popularity of electronic consent. Unfortunately, existing electronic consent systems do not pay sufficient attention to those issues. They mainly rely on conventional password based authentication to verify the identity of an electronic consent user, which is far from being sufficient given that identity theft threat is real and significant in reality. In this paper, we present a security enhanced electronic consent model called USign. USign enhances the identity protection and authentication for electronic consent systems by leveraging handwritten signatures everyone is familiar with and mobile computing technologies that are becoming ubiquitous. We developed a prototype of USign and conducted preliminary evaluation on accuracy and usability of signature verification. Our experimental results show the feasibility of the proposed model.

摘要

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引用本文的文献

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AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:80-88. eCollection 2020.
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Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness.健康信息交换中患者及参与者的身份验证与医学研究同意书:隐私保护、尊重自主权及可信度的关键步骤
Front Genet. 2018 Jun 1;9:167. doi: 10.3389/fgene.2018.00167. eCollection 2018.
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SegAuth: A Segment-based Approach to Behavioral Biometric Authentication.
SegAuth:一种基于片段的行为生物特征认证方法。
IEEE Conf Commun Netw Secur. 2016 Oct;2016:1-9. doi: 10.1109/CNS.2016.7860464. Epub 2017 Feb 23.