Privacy-preserving iris authentication using fully homomorphic encryption

被引:33
|
作者
Morampudi, Mahesh Kumar [1 ,2 ]
Prasad, Munaga V. N. K. [1 ]
Raju, U. S. N. [2 ]
机构
[1] Inst Dev & Res Banking Technol, Rd 1,Castle Hills, Hyderabad 500057, India
[2] Natl Inst Technol, Warangal 506004, Telangana, India
关键词
Biometric authentication; Homomorphic encryption; Rotation-invariant; Batching scheme; BIOMETRICS;
D O I
10.1007/s11042-020-08680-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Rapid advancement in technology has led to the use of biometric authentication in every field. In particular, from the past few years, iris recognition systems has gained overwhelming advancement over other biometric traits due to its stability and uniqueness. Directly storing the templates into a centralized server leads to privacy concerns. Many state-of-the-art iris authentication systems based on cancelable biometrics and bio-cryptosystems have been introduced to provide security for the iris templates. However, these works suffer from accuracy loss relative to unprotected systems, or they require auxiliary data (AD), which compromise the privacy of the templates and security of the system. To address this, we propose a novel privacy-preserving iris authentication using fully homomorphic encryption which ensures the confidentiality of the templates and restricts the leakage of data from the templates. Our method improves the recognition accuracy by generating rotation invariant iris codes and reduces the computational time by using the batching scheme. Our approach satisfies all the requirements specified in the ISO/IEC 24745 standard. The proposed method has experimented on four benchmark publicly available iris databases which illustrate that our method can be practically achievable with no loss in the accuracy and preserve the privacy of the iris templates. Our method encrypts and computes the Hamming distance of 2560-dimensional iris features in about 0.0185 seconds only with an equal error rate value of 0.19% for CASIA-V 1.0 database.
引用
收藏
页码:19215 / 19237
页数:23
相关论文
共 50 条
  • [1] Privacy-preserving iris authentication using fully homomorphic encryption
    Mahesh Kumar Morampudi
    Munaga V. N. K. Prasad
    U. S. N. Raju
    [J]. Multimedia Tools and Applications, 2020, 79 : 19215 - 19237
  • [2] Privacy-preserving biometrics authentication systems using fully homomorphic encryption
    Torres, Wilson Abel Alberto
    Bhattacharjee, Nandita
    Srinivasan, Bala
    [J]. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS, 2015, 11 (02) : 151 - 168
  • [3] Privacy-Preserving Palm Print Authentication using Homomorphic Encryption
    Im, Jong-Hyuk
    Choi, JinChun
    Nyang, DaeHun
    Lee, Mun-Kyu
    [J]. 2016 IEEE 14TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 14TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 2ND INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/DATACOM/CYBERSC, 2016, : 878 - 881
  • [4] Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption
    Jumonji, Seiya
    Sakai, Kazuya
    Sun, Min-Te
    Ku, Wei-Shinn
    [J]. 2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1551 - 1552
  • [5] Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption
    Jumonji, Seiya
    Sakai, Kazuya
    Sun, Min-Te
    Ku, Wei-Shinn
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (03) : 2961 - 2974
  • [6] Efficient Privacy-Preserving Fingerprint-Based Authentication System Using Fully Homomorphic Encryption
    Kim, Taeyun
    Oh, Yongwoo
    Kim, Hyoungshick
    [J]. SECURITY AND COMMUNICATION NETWORKS, 2020, 2020
  • [7] Privacy-Preserving Keystroke Analysis using Fully Homomorphic Encryption & Differential Privacy
    Loya, Jatan
    Bana, Tejas
    [J]. 2021 INTERNATIONAL CONFERENCE ON CYBERWORLDS (CW 2021), 2021, : 291 - 294
  • [8] On Fully Homomorphic Encryption for Privacy-Preserving Deep Learning
    Hernandez Marcano, Nestor J.
    Moller, Mads
    Hansen, Soren
    Jacobsen, Rune Hylsberg
    [J]. 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [9] Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
    Ono, Shinji
    Takata, Jun
    Kataoka, Masaharu
    Tomohiro, I
    Shin, Kilho
    Sakamoto, Hiroshi
    [J]. ALGORITHMS, 2022, 15 (07)
  • [10] Privacy-preserving genotype imputation with fully homomorphic encryption
    Gursoy, Gamze
    Chielle, Eduardo
    Brannon, Charlotte M.
    Maniatakos, Michail
    Gerstein, Mark
    [J]. CELL SYSTEMS, 2022, 13 (02) : 173 - +