Privacy-Protected Human Identification in Encrypted/Transformed Domains

加密/转换域中受隐私保护的人类身份识别

• 批准号: EP/P009727/2
• 负责人: Richard Jiang
• 金额: $ 1.46万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP009727%2F2
• 关键词: Privacy; Protected; Human; Identification; Encrypted

Biometrics has been widely utilized in the past two decades in many areas such as healthcare, banking, surveillance, and security control. Given the increased uptake of internet and mobile computing globally, many companies have been turning to biometric privacy and security to ensure secure communication. However, biometric verification over third-party or public network servers may be abusively exploited in an unauthorized way. To protect the privacy and improve the security, it has been advocated to carry out biometric verification in encrypted or transformed domains, where privacy and security can be more effectively guaranteed. The basic idea behind the project is that the biometrics in the irreversible encrypted/transformed domains contains exactly the same amount of information as its original one, and hence one can establish a pattern recognition methodology to determine/extract useful information from chaotic signals in encrypted/transformed domains. This First Grant Scheme project aims to investigate how to discover and evaluate the information from chaotic signals for discriminative power, and develop robust pattern recognition schemes for biometric/multi-biometric verification in encrypted/transformed domains. The proposed methods/schemes will be vigorously validated over typical wild face/speech/gait datasets, and two practical demo systems (biometric banking and pedestrian profiling) will be designed and tested in real world environments.The project will focus on both theoretical understanding of chaotic information and application-specific exploitation of chaotic pattern recognition. Considering multiple data structures hidden beneath a set of given chaotic signals, I will develop a robust way to find out the underlying various data structures for data understanding, clustering and classification. On the other side, given a specific issue such as encrypted/transformed biometric verification, one need to examine the generic theoretic findings in this specific topic and develop a robust scheme for biometric human identification.The work of this project is within the areas of signal processing, machine learning and pattern analysis. The research on encryted/transformed biometric verification has come from the practical new needs of the UK's emerging new businesses. The project will provide the understanding needed to allow the future development of robust biometric verification methods with novel applications.

在过去的二十年中，生物识别技术在医疗保健、银行、监控和安全控制等许多领域得到了广泛的应用。鉴于全球互联网和移动的计算的日益普及，许多公司一直在转向生物特征隐私和安全，以确保安全通信。然而，第三方或公共网络服务器上的生物特征验证可能会以未经授权的方式被滥用。为了保护隐私和提高安全性，一直提倡在加密或转换的域中进行生物特征验证，在这些域中可以更有效地保证隐私和安全。该项目背后的基本思想是，在不可逆的加密/变换域中的生物特征包含与其原始信息完全相同的信息量，因此可以建立一种模式识别方法来确定/提取加密/变换域中混沌信号的有用信息。这个第一个资助计划项目旨在研究如何发现和评估混沌信号中的信息，以获得区分能力，并在加密/转换域中开发用于生物特征/多生物特征验证的鲁棒模式识别方案。该项目将在典型的野生人脸/语音/步态数据集上对所提出的方法/方案进行有力的验证，并将设计两个实际的演示系统（生物特征银行和行人轮廓），并在真实的世界环境中进行测试。该项目将侧重于混沌信息的理论理解和混沌模式识别的应用开发。考虑到隐藏在一组给定混沌信号下的多个数据结构，我将开发一种鲁棒的方法来找出底层的各种数据结构，以进行数据理解，聚类和分类。另一方面，针对一个特定的问题，如加密/转换生物特征验证，需要研究这个特定主题的一般理论结果，并开发一个强大的生物特征人体识别方案。本项目的工作范围包括信号处理，机器学习和模式分析。加密/转换生物特征验证的研究来自于英国新兴业务的实际新需求。该项目将提供所需的理解，以便未来开发具有新应用的强大生物特征验证方法。

项目成果

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

• DOI: 10.1016/j.aej.2022.10.053
• 发表时间: 2022-11-02
• 期刊: Alexandria Engineering Journal
• 影响因子: 6.8

Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection

• DOI: 10.3390/rs14153680
• 发表时间: 2022-08-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Dinakaran，Ranjith;Zhang，Li;Jiang，Richard
• 通讯作者: Jiang，Richard

Deep Learning-Based Automated Forest Health Diagnosis From Aerial Images

• DOI: 10.1109/access.2020.3012417
• 发表时间: 2020-07
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Chia-Yen Chiang;Chloe Barnes;P. Angelov;Richard Jiang

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

• DOI: 10.1109/jiot.2023.3279950
• 发表时间: 2023-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Abdullah Alharthi;Q. Ni;Richard Jiang;Mohammad Ayoub Khan

Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

• DOI: 10.1109/ijcnn.2019.8851859
• 发表时间: 2019-05
• 期刊: 2019 International Joint Conference on Neural Networks (IJCNN)
• 作者: Ranjith Dinakaran;Philip Easom;Li Zhang;A. Bouridane;Richard M. Jiang;E. Edirisinghe