IEICE TRANSACTIONS on Fundamentals
Fuzzy Biometric-Based Encryption for Encrypted Data in the Cloud
Summary :
Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.5 pp.1257-1261
- Publication Date
- 2017/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1257
- Type of Manuscript
- LETTER
- Category
- Cryptography and Information Security
Authors
Qing WU
Xi'An University of Posts and Telecommunications
Leyou ZHANG
Xidian University
Jingxia ZHANG
Xidian University
Keyword
Latest Issue
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Qing WU, Leyou ZHANG, Jingxia ZHANG, "Fuzzy Biometric-Based Encryption for Encrypted Data in the Cloud" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 5, pp. 1257-1261, May 2017, doi: 10.1587/transfun.E100.A.1257.
Abstract: Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1257/_p
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@ARTICLE{e100-a_5_1257,
author={Qing WU, Leyou ZHANG, Jingxia ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fuzzy Biometric-Based Encryption for Encrypted Data in the Cloud},
year={2017},
volume={E100-A},
number={5},
pages={1257-1261},
abstract={Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.},
keywords={},
doi={10.1587/transfun.E100.A.1257},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Fuzzy Biometric-Based Encryption for Encrypted Data in the Cloud
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1257
EP - 1261
AU - Qing WU
AU - Leyou ZHANG
AU - Jingxia ZHANG
PY - 2017
DO - 10.1587/transfun.E100.A.1257
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2017
AB - Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.
ER -
English
