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.

Providing Security to Ensure Biometric Identification System in Cloud

2019 ◽  
Vol 8 (3) ◽  
pp. 1-5
Author(s):  
Bhuvaneswari Kotte ◽  
T. Sirisha Madhuri
Keyword(s):  
Cloud Computing ◽  
Security Analysis ◽  
Experimental Results ◽  
Biometric Identification ◽  
Identification System ◽  
Biometric Data ◽  
Cloud Server ◽  
Encrypted Database ◽  
Identification Procedures

Biometric identification has rapidly growing in recent years. With the development of cloud computing, database owners are incentivized to outsource the bulk size of biometric data and identification tasks to the cloud to liberate the costly storage and computation costs, which however brings potential attacks to users’ privacy. In this paper, we propose an adequate and security to keep biometric identification outsourcing scheme. Categorically, the biometric data is encrypted and outsourced to the cloud server. To get a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud implements identification operations over the encrypted database and returns the result to the database owner. An exhaustive security analysis indicated the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with antecedent protocols, experimental results show the proposed scheme achieves a better performance in both preparation and identification procedures.

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

An examination on data integrity auditing patterns in cloud computing

2018 ◽  
Vol 7 (1.9) ◽  
pp. 200
Author(s):  
T A.Mohanaprakash ◽  
J Andrews
Keyword(s):  
Cloud Computing ◽  
Information Security ◽  
Data Integrity ◽  
Cloud Service ◽  
New Approach ◽  
Computing Services ◽  
Security Challenges ◽  
Cloud Server ◽  
State Of Affairs ◽  
Dynamic Updates

Cloud computing is associate inclusive new approach on however computing services square measure made and utilized. Cloud computing is associate accomplishment of assorted styles of services that has attracted several users in today’s state of affairs. The foremost enticing service of cloud computing is information outsourcing, because of this the information homeowners will host any size of information on the cloud server and users will access the information from cloud server once needed. A dynamic outsourced auditing theme that cannot solely defend against any dishonest entity and collision, however conjointly support verifiable dynamic updates to outsourced information. The new epitome of information outsourcing conjointly faces the new security challenges. However, users might not totally trust the cloud service suppliers (CSPs) as a result of typically they may be dishonest. It's tough to work out whether or not the CSPs meet the customer’s expectations for information security. Therefore, to with success maintain the integrity of cloud information, several auditing schemes are projected. Some existing integrity ways will solely serve for statically archived information and a few auditing techniques is used for the dynamically updated information. The analyzed numerous existing information integrity auditing schemes together with their consequences.

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.

Privacy-preserving inpainting for outsourced image

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110590
Author(s):  
Fang Cao ◽  
Jiayi Sun ◽  
Xiangyang Luo ◽  
Chuan Qin ◽  
Ching-Chun Chang
Keyword(s):  
Euclidean Distance ◽  
Image Inpainting ◽  
Privacy Preserving ◽  
Weighted Mean ◽  
Encrypted Image ◽  
Cloud Server ◽  
Before And After ◽  
Current Block ◽  
Secure Cloud Computing

In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain.

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