Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

Higo, Haruna; Isshiki, Takaaki; Obana, Satoshi

doi:10.1007/978-3-319-31301-6_3

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…In these schemes, the biometric identification scheme is considered to be a two-party system, where the data owner takes charge of biometric dataset management and template matching. Most of these schemes are designed based on the secure computation protocol [18][19][20] and homomorphic encryption [9,21,22] techniques. Although the privacy-preserving is achieved in these schemes, the data owner is required to be equipped with powerful computing ability and remarkable storage capacity in these schemes, which can hardly be satisfied in most application scenarios and thus makes these schemes unpractical.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, researchers have proposed many schemes [6][7][8][9][10][11][12][13] to achieve privacy-preserving biometric recognition. Unfortunately, most of these schemes [6][7][8][9][10] work in a basic way, which means that they just traverse the whole dataset to get the identification result, and no optimization tactics are taken to accelerate the searching process. Consequently, a huge computing burden is brought to the cloud when it handles too many identification requests simultaneously, which makes these schemes inefficient and unpractical.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhu

Zhang

et al. 2021

Security and Communication Networks

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhu

Zhang

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Apart from this, another drawback of the scheme lies in the low-accuracy of the recognition result. Higo et al [279] introduced an enhanced scheme for homomorphic fingerprint authentication computations, which takes advantage of the information of fingerprint minutiae (i.e., feature points in the fingerprints). Suppose two fingerprint minutiae are denoted as ((x 1 , y 1 ), t 1 ) and ((x 2 , y 2 ), t 2 ), where the former pair in the tuple is the location of one minutia and the latter is its direction.…”

Section: ) Biometric Authenticationmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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Privacy Preserving Biometric Identification on the Bitcoin Blockchain

Sarier

2018

Cyberspace Safety and Security

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Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

Cited by 8 publications

References 16 publications

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

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

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

Privacy Preserving Biometric Identification on the Bitcoin Blockchain

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