Deep Secure Quantization: On secure biometric hashing against similarity-based attacks

Chen, Yanzhi; Wan, Yan; Xie, Renjie; Wu, Chudan; Han, Guoqiang

doi:10.1016/j.sigpro.2018.09.013

Cited by 38 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The evaluation of the resistance to similarity-based attacks of the biometric LPN commitments was performed according to [14], which considers that protected templates are secure only if the mutual information between the normalized distances of the impostor data in the protected and unprotected domains is very small. In [14], mutual information is assumed to be upper bounded by the variance of the distribution of the impostor protected distances. The results obtained are shown in Fig.…”

Section: Security Analysis Of the Protected Approachmentioning

confidence: 99%

“…In the other side, most of cancelable biometric schemes apply similarity-preserving transformations, also called Locality Sensitive Hashing, in order to preserve in the protected domain the accuracy performance obtained in the unprotected domain [14] [15]. The problem is that this similarity or distance-preserving property (distances between unprotected samples are nearly the same as the distances between protected samples) can be exploited by similaritybased attacks that break these schemes.…”

Section: Introductionmentioning

confidence: 99%

“…The work in [15] confirms the vulnerability of BioHashing and Bloom-filter schemes to a Genetic Algorithm enabled similarity-based attack. The work in [14] introduces non-linearity in the transformation with the aid of a deep neural network, but this requires retraining whenever a new user is enrolled.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Arjona

Baturone

2020

IEEE Access

View full text Add to dashboard Cite

Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), crossmatching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition system implemented in Matlab.

show abstract

Section: Security Analysis Of the Protected Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Arjona

Baturone

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The deep neural network is also employed in the biometric protection. To address the advent of similarity-based attacks (SA), Deep Secure Quantization (DSQ) based on neural network implementations is used to protect iris feature [31]. Deen Dayal Mohan presented a significant bit based representation for creating secure face templates with negligible degradation in the performance [32].…”

Section: Related Workmentioning

confidence: 99%

One-Factor Cancellable Palmprint Recognition Scheme Based on OIOM and Minimum Signature Hash

Wang

2019

IEEE Access

View full text Add to dashboard Cite

The traditional Cancellable biometrics scheme needs another key or token to generate the revocable template, which usually suffers from the token-stolen problem. To solve this problem, a one-factor cancellable palmprint biometric recognition scheme based on Orthogonal Index of Maximum (OIOM) hash and Minimum Signature Hash (MSH) is proposed to generate pseudonymous identifier. Firstly, to improve the efficiency and effectiveness, a parallel structure is designed to obtain Orthogonal Gaussian random projection(GRP)matrices, which is employed to generate the OIOM hash code. Secondly, a random binary string is XOR the binary OIOM hash code to construct the helper data and it is stored in the database. Meanwhile, this string is hashed by MSH to get the final pseudonymous identifier. Lastly, during matching stage, based on helper data and a palmprint images, another pseudonymous identifier is generated to recognition. This implies that, just one factor, a palmprint of user, is needed in the matching stage, and therefore the privacy of user is preserved. To evaluate the proposed scheme, PolyU database and touchless TJU database are used in experiments. The noninvertible, renewability, unlinkability and several security attacks of the proposed scheme are analyzed. The experiment results and analysis show that the proposed scheme has strong security on the basis of maintaining palmprint recognition performance.INDEX TERMS Biometric recognition, cancellable palmprint feature, orthogonal index of maximum, minimum signature hash.

show abstract

“…More recently, the proposal of Aness and Chen used discriminative binary feature learning and quantization [67], and Yuliana, Wirawan, and Suwadi [68] combined pre-processing with multi-level quantization. Furthermore, Chen, Wo, Xie, Wu, and Han improved quantization techniques against leakage and similarity-based attacks [69].…”

mentioning

confidence: 99%

Cryptobiometrics for the Generation of Cancellable Symmetric and Asymmetric Ciphers with Perfect Secrecy

Vera

Ávila

2020

Mathematics

View full text Add to dashboard Cite

Security objectives are the triad of confidentiality, integrity, and authentication, which may be extended with availability, utility, and control. In order to achieve these goals, cryptobiometrics is essential. It is desirable that a number of characteristics are further met, such as cancellation, irrevocability, unlinkability, irreversibility, variability, reliability, and biometric bit-length. To this end, we designed a cryptobiometrics system featuring the above-mentioned characteristics, in order to generate cryptographic keys and the rest of the elements of cryptographic schemes—both symmetric and asymmetric—from a biometric pattern or template, no matter the origin (i.e., face, fingerprint, voice, gait, behaviour, and so on). This system uses perfect substitution and transposition encryption, showing that there exist two systems with these features, not just one (i.e., the Vernam substitution cipher). We offer a practical application using voice biometrics by means of the Welch periodogram, in which we achieved the remarkable result of an equal error rate of (0.0631, 0.9361). Furthermore, by means of a constructed template, we were able to generate the prime value which specifies the elliptic curve describing all other data of the cryptographic scheme, including the private and public key, as well as the symmetric AES key shared between the templates of two users.

show abstract

Deep Secure Quantization: On secure biometric hashing against similarity-based attacks

Cited by 38 publications

References 14 publications

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

One-Factor Cancellable Palmprint Recognition Scheme Based on OIOM and Minimum Signature Hash

Cryptobiometrics for the Generation of Cancellable Symmetric and Asymmetric Ciphers with Perfect Secrecy

Contact Info

Product

Resources

About