Privacy-Preserving Similarity Evaluation and Application to Remote Biometrics Authentication

Kikuchi, Hiroaki; Nagai, K.; Ogata, Wakaha; Nishigaki, Masakatsu

doi:10.1007/978-3-540-88269-5_2

Cited by 15 publications

(19 citation statements)

References 9 publications

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“…e) Zero-Knowledge Proofs of Knowledge: On a high level, a zero-knowledge proof of knowledge is a two-party protocol between a prover and a verifier, which allows the prover to convince the verifier that it knows some secret values that satisfy a given relation (proof of knowledge property), without the verifier being able to learn anything about them (zero-knowledge property) [37]. Such proofs are extensively used in applied cryptography as they allow protocol designers to enforce a protocol participant to assure other parties that its actions are consistent with its internal knowledge state, e.g., [38], [39], [40], [41], [42].…”

Section: Input Language and Featuresmentioning

confidence: 98%

cPLC — A cryptographic programming language and compiler

Bangerter

Krenn

Seifriz

et al. 2011

2011 Information Security for South Africa

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Cryptographic two-party protocols are used ubiquitously in everyday life. While some of these protocols are easy to understand and implement (e.g., key exchange or transmission of encrypted data), many of them are much more complex (e.g., ebanking and e-voting applications, or anonymous authentication and credential systems).For a software engineer without appropriate cryptographic skills the implementation of such protocols is often difficult, time consuming and error-prone. For this reason, a number of compilers supporting programmers have been published in recent years. However, they are either designed for very specific cryptographic primitives (e.g., zero-knowledge proofs of knowledge), or they only offer a very low level of abstraction and thus again demand substantial mathematical and cryptographic skills from the programmer. Finally, some of the existing compilers do not produce executable code, but only metacode which has to be instantiated with mathematical libraries, encryption routines, etc. before it can actually be used.In this paper we present a cryptographically aware compiler which is equally useful to cryptographers who want to benchmark protocols designed on paper, and to programmers who want to implement complex security sensitive protocols without having to understand all subtleties. Our tool offers a high level of abstraction and outputs well-structured and documented Java code. We believe that our compiler can contribute to shortening the development cycles of cryptographic applications and to reducing their error-proneness.

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Section: Input Language and Featuresmentioning

confidence: 98%

cPLC — A cryptographic programming language and compiler

Bangerter

Krenn

Seifriz

et al. 2011

2011 Information Security for South Africa

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“…We highlight detailed differences between our proposed learning model and the existing works. Encryption methods can be employed to address the security and privacy concerns in machine learning 16,17 .…”

Section: Related Workmentioning

confidence: 99%

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

Çatak¹,

Mustaçoğlu²

2018

IJCIS

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The training techniques of the distributed machine learning approach replace the traditional methods with a cloud computing infrastructure and provide flexible computing services to clients. Moreover, machine learning-based classification methods are used in many diverse applications such as medical predictions, speech/face recognition, and financial applications. Most of the application areas require security and confidentiality for both the data and the classifier model. In order to prevent the risk of confidential data disclosure while outsourcing the data analysis, we propose a privacy-preserving protocol approach for the extreme learning machine algorithm and give private classification protocols. The proposed protocols compute the hidden layer output matrix H in an encrypted form by using a distributed multi-party computation (or cloud computing model) approach. This paper shows how to build a privacy-preserving classification model from encrypted data.

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“…The downside of this development is that the problems of information security and individual's privacy are becoming extremely serious. Biometric authentication can be used to ensure information security (Kikuchi et al 2010;Choi et al 2012;Tistarelli and Schouten 2011). However, it has a lot of problems, such as the finite number of biometric traits and the information leakage caused by stolen biometric templates.…”

Section: Introductionmentioning

confidence: 99%

High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia

Liang

You

et al. 2015

Soft Comput

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Biometric identification has caused a multitude of problems in the networking environment, such as the storage of user's biometric template and the leakage of user's privacy, therefore, biometric encryption has been the focus of the recent studies which are based on Fuzzy Vault, Fuzzy Commitment, and dynamic key generation. However, due to fuzzy information inherent biological characteristics, essentially deterministic analysis techniques, Fuzzy Vault, and Fuzzy Commitment have been accused of stored biometric templates and short keys. Fuzzy Information Processing needs suitable technology, such as fuzzy logic, to obtain better results. In this paper, we propose a new fingerprint encryption scheme which utilizes the high-dimension space projection. Unlike the reliance on biometric templates in Fuzzy Vault-based scheme or "encrypted" templates in Fuzzy Commitment-based scheme, this new scheme, similar to the Communicated by V. Loia.

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Privacy-Preserving Similarity Evaluation and Application to Remote Biometrics Authentication

Cited by 15 publications

References 9 publications

cPLC — A cryptographic programming language and compiler

cPLC — A cryptographic programming language and compiler

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia

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Privacy-Preserving Similarity Evaluation and Application to Remote Biometrics Authentication

Cited by 15 publications

References 9 publications

cPLC &#x2014; A cryptographic programming language and compiler

cPLC &#x2014; A cryptographic programming language and compiler

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia

Contact Info

Product

Resources

About

cPLC — A cryptographic programming language and compiler

cPLC — A cryptographic programming language and compiler