scholarly journals A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 164
Author(s):  
Tong-Yuen Chai ◽  
Bok-Min Goi ◽  
Yong-Haur Tay ◽  
and Zhe Jin
Keyword(s):  
Data Privacy ◽  
Iris Recognition ◽  
Large Scale ◽  
Security Analysis ◽  
Security And Privacy ◽  
Biometric Data ◽  
Alignment Free ◽  
Biometric Template ◽  
Significant Performance ◽  
Template Protection

Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme.

scholarly journals Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 910
Author(s):  
Tong-Yuen Chai ◽  
Bok-Min Goi ◽  
Wun-She Yap
Keyword(s):  
Data Privacy ◽  
Recognition Performance ◽  
Generalized Solution ◽  
Performance Degradation ◽  
Public Confidence ◽  
Data Types ◽  
Privacy And Security ◽  
Biometric Data ◽  
Biometric Template ◽  
Template Protection

Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.

scholarly journals Transmission of Secure Biometric Data for Network-based User Authentication

2020 ◽  
Author(s):  
M Khan
Keyword(s):  
Large Scale ◽  
User Authentication ◽  
Public Key Infrastructure ◽  
The Other ◽  
Security And Privacy ◽  
Public Key ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Biometric Template ◽  
Secure Transmission

The large-scale utilization of biometric authentication systems creates a demand for effective and reliable security and privacy of its data. Biometric data is not secret and if compromised, it can have catastrophic effects on the integrity of the whole verification system. To address these issues, this paper presents a novel encryption and watermarking method by using public key infrastructure for the secure transmission of biometric data over network. Encryption is applied on the biometric template before embedding as a watermark to make it more secure and robust and then, it is hid into the cover image. Experimental results show that the security, performance, and accuracy of the presented method is encouraging comparable with the other methods found in the current literature.

scholarly journals Transmission of Secure Biometric Data for Network-based User Authentication

2020 ◽  
Author(s):  
M Khan
Keyword(s):  
Large Scale ◽  
User Authentication ◽  
Public Key Infrastructure ◽  
The Other ◽  
Security And Privacy ◽  
Public Key ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Biometric Template ◽  
Secure Transmission

The large-scale utilization of biometric authentication systems creates a demand for effective and reliable security and privacy of its data. Biometric data is not secret and if compromised, it can have catastrophic effects on the integrity of the whole verification system. To address these issues, this paper presents a novel encryption and watermarking method by using public key infrastructure for the secure transmission of biometric data over network. Encryption is applied on the biometric template before embedding as a watermark to make it more secure and robust and then, it is hid into the cover image. Experimental results show that the security, performance, and accuracy of the presented method is encouraging comparable with the other methods found in the current literature.

scholarly journals Privacy-Preserving Fingerprint Authentication Using D-H Key Exchange and Secret Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiyong Wang ◽  
Mingjun Luo ◽  
Yong Ding
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Key Exchange ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Biometric Data ◽  
Diffie Hellman

Biometric based remote authentication has been widely deployed. However, there exist security and privacy issues to be addressed since biometric data includes sensitive information. To alleviate these concerns, we design a privacy-preserving fingerprint authentication technique based on Diffie-Hellman (D-H) key exchange and secret sharing. We employ secret sharing scheme to securely distribute fragments of critical private information around a distributed network or group, which softens the burden of the template storage center (TSC) and the users. To ensure the security of template data, the user’s original fingerprint template is stored in ciphertext format in TSC. Furthermore, the D-H key exchange protocol allows TSC and the user to encrypt the fingerprint template in each query using a random one-time key, so as to protect the user’s data privacy. Security analysis indicates that our scheme enjoys indistinguishability against chosen-plaintext attacks and user anonymity. Through experimental analysis, we demonstrate that our scheme can provide secure and accurate remote fingerprint authentication.

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.

Biometric Template Protection Using Spiral Cube: Performance and Security Analysis

2016 ◽  
Vol 25 (01) ◽  
pp. 1550027 ◽  
Author(s):  
Chouaib Moujahdi ◽  
George Bebis ◽  
Sanaa Ghouzali ◽  
Mounia Mikram ◽  
Mohammed Rziza
Keyword(s):  
Chaotic Behavior ◽  
Logistic Map ◽  
Security Analysis ◽  
Personal Data ◽  
Random Projection ◽  
Biometric Template ◽  
Template Protection ◽  
And Performance ◽  
Biometric Templates ◽  
Biometric Template Protection

Personal authentication systems based on biometrics have given rise to new problems and challenges related to the protection of personal data, issues of less concern in traditional authentication systems. The irrevocability of biometric templates makes biometric systems very vulnerable to several attacks. In this paper we present a new approach for biometric template protection. Our objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance. In this context, we use the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity. The proposed approach has been evaluated and compared with Biohashing and BioPhasor using a rigorous security analysis. Our extensive experimental results using several databases (e.g., face, finger-knuckle and iris), show that the proposed technique has the ability to preserve and increase the performance of protected systems. Moreover, it is demonstrated that the security of the proposed approach is sufficiently robust to possible attacks keeping an acceptable balance between discrimination, diversity and non-invertibility.

Differential Privacy Approach for Big Data Privacy in Healthcare

2017 ◽  
pp. 191-213 ◽  
Author(s):  
Marmar Moussa ◽  
Steven A. Demurjian
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
Large Scale ◽  
Differential Privacy ◽  
Security And Privacy ◽  
Instrumental Case Study ◽  
Large Scale Data ◽  
Private Data

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

scholarly journals Biometric iris templates security based on secret image sharing and chaotic maps

2022 ◽  
Vol 12 (1) ◽  
pp. 339
Author(s):  
Marwa Fadhel Jassim ◽  
Wafaa mohammed Saeed Hamzah ◽  
Abeer Fadhil Shimal
Keyword(s):  
Safety Issue ◽  
Recognition System ◽  
Security And Privacy ◽  
Secret Image Sharing ◽  
Data Set ◽  
Secret Image ◽  
Image Sharing ◽  
Biometric Template ◽  
Behavioural Characteristics ◽  
Template Protection

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

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