Feature-Level Fusion of Physiological Parameters to be Used as Cryptographic Keys

被引:0
|
作者
Altop, Duygu Karaoglan [1 ]
Levi, Albert [1 ]
Tuzcu, Volkan [2 ]
机构
[1] Sabanci Univ, Dept Comp Sci & Engn, Istanbul, Turkey
[2] Istanbul Medipol Univ, Dept Pediat Cardiol, Istanbul, Turkey
关键词
Cryptographic Key Generation; Body Area Network Security; Physiological Signals; Key Agreement; Bio-cryptography; Feature-Level Fusion;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this paper, we propose two novel feature-level fused physiological parameter generation techniques: (i) concat-fused physiological parameter generation, and (ii) xor-fused physiological parameter generation, output of which can be used to secure the communication among the biosensors in Body Area Network (BAN). In these physiological parameter generation techniques, we combine a time-domain physiological parameter with a frequency-domain physiological parameter, in order to achieve robust performance compared to their singular versions. We analyze both the performance and the quality of the outcomes. Our results show that we generate good candidates of physiological parameters that can be used as cryptographic keys to provide security for the intra-network communication in BANs.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Robust Human Activity Recognition Using Multimodel Feature-Level Fusion
    Ehatisham-Ul-Haq, Muhammad
    Javed, Ali
    Azam, Muhammad Awais
    Malik, Hafiz M. A.
    Irtaza, Aun
    Lee, Ik Hyun
    Mahmood, Muhammad Tariq
    IEEE ACCESS, 2019, 7 : 60736 - 60751
  • [42] Feature-level Data Fusion for Energy Consumption Analytics in Additive Manufacturing
    Hu, Fu
    Liu, Ying
    Qin, Jian
    Sun, Xianfang
    Witherell, Paul
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2020, : 612 - 617
  • [43] A Feature-Level Fusion Scheme Based on Eigen Theory for Multimodal Biometrics
    Chen, Wen-Shiung
    Jeng, Ren-He
    Chen, Yen-Feng
    IETE TECHNICAL REVIEW, 2022, 39 (05) : 1081 - 1091
  • [44] Ann trained and WOA optimized feature-level fusion of iris and fingerprint
    Kumar, Tajinder
    Bhushan, Shashi
    Jangra, Surender
    MATERIALS TODAY-PROCEEDINGS, 1600, 0 (00):
  • [45] Subspace framework for feature-level fusion with its application to handmetric verification
    Li, Qiang
    Qiu, Zhengding
    Sun, Dongmei
    Zhang, Yanqiang
    2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4, 2006, : 2416 - +
  • [46] A Feature-Level Fusion Scheme Based on Eigen Theory for Multimodal Biometrics
    Chen, Wen-Shiung
    Jeng, Ren-He
    Chen, Yen-Feng
    IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), 2022, 39 (05): : 1081 - 1091
  • [47] A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization
    Liu, Minxiang
    Wang, Yuhao
    Leung, Henry
    Yu, Jiangnan
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2013, 2013
  • [48] 3S data feature-level fusion by neural network
    Sun, YN
    Han, M
    Xu, SG
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 3920 - 3923
  • [49] Schizophrenia diagnosis using innovative EEG feature-level fusion schemes
    Goshvarpour, Atefeh
    Goshvarpour, Ateke
    PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2020, 43 (01) : 227 - 238
  • [50] Multimodal Biometrics System Using Feature-Level Fusion of Iris and Fingerprint
    Khoo, Yik-Herng
    Goi, Bok-Min
    Chai, Tong-Yuen
    Lai, Yen-Lung
    Jin, Zhe
    ICAIP 2018: 2018 THE 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN IMAGE PROCESSING, 2018, : 6 - 10