Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing

Chang Xu; Lvhan Zhang; Liehuang Zhu; Chuan Zhang; Kashif Sharif

doi:10.1109/TrustCom53373.2021.00063

Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing

Chang Xu, Lvhan Zhang, Liehuang Zhu^*, Chuan Zhang, Kashif Sharif

^*Corresponding author for this work

School of Cyberspace Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Biometrics identification has been used in a growing number of fields in recent years, since it is more secure, classified and convenient. With the development of cloud computing, database systems are able to upload large amounts of biometric data to cloud server for storage and identification to save local memory and improve computational efficiency. However, this involves potential privacy concerns because of the introduction of third-party platforms. In this paper, we achieve computational and communication efficiency in biometric identification, while preserving the privacy of data. Specifically, the database system firstly encrypts all biometric data and query data. Then, it sends the cipher to a cloud server to carry out matching tasks. Finally, the cloud server returns the index of final matches to the system so that it can check whether the biometric vector is legal or not. Detailed security analysis indicates that the proposed scheme can resist powerful attacks. Beyond that, Experiments show that the scheme is more efficient in computation and communication than stat of art biometric identification schemes.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
Editors	Liang Zhao, Neeraj Kumar, Robert C. Hsu, Deqing Zou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	363-370
Number of pages	8
ISBN (Electronic)	9781665416580
DOIs	https://doi.org/10.1109/TrustCom53373.2021.00063
Publication status	Published - 2021
Event	20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 - Shenyang, China Duration: 20 Oct 2021 → 22 Oct 2021

Publication series

Name	Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021

Conference

Conference	20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
Country/Territory	China
City	Shenyang
Period	20/10/21 → 22/10/21

Keywords

Biometric Identification
Cloud Server
Data out-sourcing
Privacy-preserving

Access to Document

10.1109/TrustCom53373.2021.00063

Cite this

Xu, C., Zhang, L., Zhu, L., Zhang, C., & Sharif, K. (2021). Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing. In L. Zhao, N. Kumar, R. C. Hsu, & D. Zou (Eds.), Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 (pp. 363-370). (Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TrustCom53373.2021.00063

Xu, Chang ; Zhang, Lvhan ; Zhu, Liehuang et al. / Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing. Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021. editor / Liang Zhao ; Neeraj Kumar ; Robert C. Hsu ; Deqing Zou. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 363-370 (Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021).

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title = "Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing",

abstract = "Biometrics identification has been used in a growing number of fields in recent years, since it is more secure, classified and convenient. With the development of cloud computing, database systems are able to upload large amounts of biometric data to cloud server for storage and identification to save local memory and improve computational efficiency. However, this involves potential privacy concerns because of the introduction of third-party platforms. In this paper, we achieve computational and communication efficiency in biometric identification, while preserving the privacy of data. Specifically, the database system firstly encrypts all biometric data and query data. Then, it sends the cipher to a cloud server to carry out matching tasks. Finally, the cloud server returns the index of final matches to the system so that it can check whether the biometric vector is legal or not. Detailed security analysis indicates that the proposed scheme can resist powerful attacks. Beyond that, Experiments show that the scheme is more efficient in computation and communication than stat of art biometric identification schemes.",

keywords = "Biometric Identification, Cloud Server, Data out-sourcing, Privacy-preserving",

author = "Chang Xu and Lvhan Zhang and Liehuang Zhu and Chuan Zhang and Kashif Sharif",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 ; Conference date: 20-10-2021 Through 22-10-2021",

year = "2021",

doi = "10.1109/TrustCom53373.2021.00063",

language = "English",

series = "Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Liang Zhao and Neeraj Kumar and Hsu, {Robert C.} and Deqing Zou",

booktitle = "Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021",

address = "United States",

}

Xu, C, Zhang, L, Zhu, L , Zhang, C & Sharif, K 2021, Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing. in L Zhao, N Kumar, RC Hsu & D Zou (eds), Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021. Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, Institute of Electrical and Electronics Engineers Inc., pp. 363-370, 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, Shenyang, China, 20/10/21. https://doi.org/10.1109/TrustCom53373.2021.00063

Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing. / Xu, Chang; Zhang, Lvhan; Zhu, Liehuang et al.
Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021. ed. / Liang Zhao; Neeraj Kumar; Robert C. Hsu; Deqing Zou. Institute of Electrical and Electronics Engineers Inc., 2021. p. 363-370 (Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing

AU - Xu, Chang

AU - Zhang, Lvhan

AU - Zhu, Liehuang

AU - Zhang, Chuan

AU - Sharif, Kashif

PY - 2021

Y1 - 2021

N2 - Biometrics identification has been used in a growing number of fields in recent years, since it is more secure, classified and convenient. With the development of cloud computing, database systems are able to upload large amounts of biometric data to cloud server for storage and identification to save local memory and improve computational efficiency. However, this involves potential privacy concerns because of the introduction of third-party platforms. In this paper, we achieve computational and communication efficiency in biometric identification, while preserving the privacy of data. Specifically, the database system firstly encrypts all biometric data and query data. Then, it sends the cipher to a cloud server to carry out matching tasks. Finally, the cloud server returns the index of final matches to the system so that it can check whether the biometric vector is legal or not. Detailed security analysis indicates that the proposed scheme can resist powerful attacks. Beyond that, Experiments show that the scheme is more efficient in computation and communication than stat of art biometric identification schemes.

AB - Biometrics identification has been used in a growing number of fields in recent years, since it is more secure, classified and convenient. With the development of cloud computing, database systems are able to upload large amounts of biometric data to cloud server for storage and identification to save local memory and improve computational efficiency. However, this involves potential privacy concerns because of the introduction of third-party platforms. In this paper, we achieve computational and communication efficiency in biometric identification, while preserving the privacy of data. Specifically, the database system firstly encrypts all biometric data and query data. Then, it sends the cipher to a cloud server to carry out matching tasks. Finally, the cloud server returns the index of final matches to the system so that it can check whether the biometric vector is legal or not. Detailed security analysis indicates that the proposed scheme can resist powerful attacks. Beyond that, Experiments show that the scheme is more efficient in computation and communication than stat of art biometric identification schemes.

KW - Biometric Identification

KW - Cloud Server

KW - Data out-sourcing

KW - Privacy-preserving

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DO - 10.1109/TrustCom53373.2021.00063

M3 - Conference contribution

AN - SCOPUS:85127374530

T3 - Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021

SP - 363

EP - 370

BT - Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021

A2 - Zhao, Liang

A2 - Kumar, Neeraj

A2 - Hsu, Robert C.

A2 - Zou, Deqing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021

Y2 - 20 October 2021 through 22 October 2021

ER -

Xu C, Zhang L, Zhu L , Zhang C, Sharif K. Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing. In Zhao L, Kumar N, Hsu RC, Zou D, editors, Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 363-370. (Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021). doi: 10.1109/TrustCom53373.2021.00063

Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing

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