scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 19025-19033 ◽  
Author(s):  
Liehuang Zhu ◽  
Chuan Zhang ◽  
Chang Xu ◽  
Ximeng Liu ◽  
Cheng Huang
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Identification Scheme

scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaopeng Yang ◽  
Hui Zhu ◽  
Songnian Zhang ◽  
Rongxing Lu ◽  
Xuesong Gao
Keyword(s):  
Homomorphic Encryption ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Large Dataset ◽  
Biometric Data ◽  
Communication Costs ◽  
Identification Scheme ◽  
Biometric Template ◽  
Cloud Servers

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

scholarly journals Biometric Identification System in Cloud Computing Using Blockchain

2019 ◽  
Vol 8 (2S11) ◽  
pp. 998-1001
Keyword(s):  
Cloud Computing ◽  
Biometric Identification ◽  
Identification System ◽  
Identification Scheme ◽  
System Usage ◽  
Cloud Server ◽  
Current Block ◽  
Block Chain ◽  
It Applications ◽  
Intended Use

Block-chain is a distributed immutable ledger technology. It consisting of blocks, and each block contains multiple transactions. Block-chain consists a secure hash, timestamp, data of the current block, and the hash value of the previous block. Block- chain records all transactions across the network so that it cannot be altered retroactively without the alteration of all subsequent blocks and the consensus of the network. Biometric identification system usage has increased as it provides an auspicious way to identify users. While compared with traditional authentication methods Biometric is more reliable and convenient. Block-chain was designed initially to solve double spending problems in peer-to-peer payment system of Bitcoin. But, since then it applications gone through the original intended use because of its properties, i.e. decentralization, immutability, no trusted authority and auditability. In this paper we propose how this Block-chain technology can have applied in biometric identification scheme like aadhar in cloud server-to make aadhar more transparent.

An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Encrypted Data

Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

2019 ◽  
Vol 140-141 ◽  
pp. 38-60 ◽  
Author(s):  
Josep Domingo-Ferrer ◽  
Oriol Farràs ◽  
Jordi Ribes-González ◽  
David Sánchez
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Sensitive Data
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