Fingerprint Template Protection Using Fuzzy Vault

Biometric based authentication can provide strong security guarantee about the identity of users. However, security of biometric data is particularly important as compromise of the data will be permanent. Cancelable biometrics store a non-invertible transformed version of the biometric data. Thus, even if the storage is compromised, the biometric data remains safe. Cancelable biometrics also provide a higher level of privacy by allowing many templates for the same biometric data and hence non-linkability of user's data stored in different databases. In this paper, we propose an approach for cancelable fingerprint templates by using the idea of fuzzy vault. By integrating the fuzzy fingerprint vault with the existing password-based authentication system, we can use a different "long and random" password for each application, and the fuzzy fingerprint vault can be changed by simply changing the password.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Theft of a template is theft of identity. Fingerprint identification is used by forensic department in identifying criminals because of its uniqueness. Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits. Fingerprint identification is commonly employed in Forensic science to support criminal investigations and in biometric systems such as civilian and commercial identification devices. The best way to identify criminals is using fingerprint verification which cannot be replaced. So such fingerprint acquired from the criminal should be safely stored and protected from hackers for future references. This paper mainly deals with protecting the criminal's record (fingerprint template). Templates represent intrinsic information about a criminal. The criminal record (Fingerprint template) should be protected from creation of physical spoof and replacement by imposter's template to gain unauthorized access. Due to these reasons, fool-proof techniques are required for securely storing the criminal fingerprint records. Fuzzy vault is a cryptographic construct that is designed to work with biometric features represented as an unordered set like minutiae points. Template protection is done by transformation based approaches and biometric cryptosystems. During transformation template is stored in a transformed form so that the original biometric template cannot be easily recovered. Fingerprint from the criminal is first preprocessed and minutiae points are extracted. Preprocessing is very essential because the creation of fuzzy vault highly depends on the quality of the input fingerprint image. Then image is binarized followed by thinning and then region of interest (ROI) is calculated finally followed by removal of false minutiae points. The remaining useful minutiae points are used in fuzzy vault generation. A polynomial is evaluated from the secret key which is combined with minutiae points to produce a secure vault. Some dummy points called chaff points are also added to increase the security. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction and the number of chaff points. In the proposed system an even more secured fuzzy vault is generated with combined features of fingerprint and palm print to enhance the security of the template stored.

Biometric-based authentication can provide a strong security guarantee of the identity of users. However, the security of biometric data is particularly important as any compromise of the biometric data will be permanent. In this paper, we propose a secure and efficient protocol to transmit fingerprint images from a fingerprint sensor to a client by exploiting the characteristics of the fingerprint images. Because the fingerprint sensor is computationally limited, a standard encryption algorithm may not be applied to the full fingerprint images in real-time to guarantee the integrity and confidentiality of the fingerprint images transmitted. To reduce the computational workload on the resource-constrained sensor, we apply the encryption algorithm to a nonce for integrity and to a specific bitplane of each pixel of the fingerprint image for confidentiality. Experimental results show that the integrity and confidentiality of the fingerprint images can be guaranteed without any leakage of the fingerprint ridge information and can be completed in real-time on embedded processors.

In recent years the security breaches and fraud transactions are increasing day by day. So there is a necessity for highly secure authentication technologies. The security of an authentication system can be strengthened by using Biometric system rather than the traditional method of authentication like Identity Cards (ID) and password which can be stolen easily. A biometric system works on biometric traits and fingerprint has the maximum share in market for providing biometric authentication as it is reliable, consistent and easy to capture. Although the biometric system is used to provide security to many applications but it is susceptible to different types of assaults too. Among all the modules of the biometric system which needs security, biometric template protection has received great consideration in the past years from the research community due to sensitivity of the biometric data stored in the form of template. A number of methods have been devised for providing template protection. Fuzzy vault is one of the cryptosystem based method of template security. The aim of fuzzy vault technique is to protect the precarious data with the biometric template in a way that only certified user can access the secret by providing valid biometric. In this paper, a modified version of fuzzy vault is presented to increase the level of security to the template and the secret key. The polynomial whose coefficients represent the key is transformed using an integral operator to hide the key where the key can no longer be derived if the polynomial is known to the attacker. The proposed fuzzy vault scheme also prevents the system from stolen key inversion attack. The results are achieved in terms of False Accept Rate (FAR), False Reject Rate (FRR), Genuine Acceptance Rate (GAR) by varying the degree of polynomial and number of biometric samples. It was calculated that for 40 users GAR was found to be 92%, 90%, 85% for degree of polynomial to be 3, 4 and 5 respectively. It was observed that increasing the degree of polynomial decreased the FAR rate, thus increasing the security

Smartphone-based biometrics authentication has been increasingly used for many popular everyday applications such as e-banking and secure access control to personal services. The use of biometric data on smartphones introduces the need for capturing and storage of biometric data such as face images. Unlike the traditional passwords used for many services, biometric data once compromised cannot be replaced. Therefore, the biometric data not only should not be stored as a raw image but also needs to be protected such that the original image cannot be reconstructed even if the biometric data is available. The transforming of raw biometric data such as face image should not decrease the comparison performance limiting the use of biometric services. It can therefore be deduced that the feature representation and the template protection scheme should be robust to have reliable smartphone biometrics. This chapter presents two variants of a new approach of template protection by enforcing the structure preserving feature representation via manifolds, followed by the hashing on the manifold feature representation. The first variant is based on the Stochastic Neighbourhood Embedding and the second variant is based on the Laplacian Eigenmap. The cancelability feature for template protection using the proposed approach is induced through inherent hashing approach relying on manifold structure. We demonstrate the applicability of the proposed approach for smartphone biometrics using a moderately sized face biometric data set with 94 subjects captured in 15 different and independent sessions in a closed-set scenario. The presented approach indicates the applicability with a low Equal Error Rate, \(EER = 0.65\%\) and a Genuine Match Rate, \(GMR = 92.10\%\) at False Match Rate (FMR) of \(0.01\%\) for the first variant and the second variant provides \(EER = 0.82\%\) and \(GMR = 89.45\%\) at FMR of \(0.01\%\). We compare the presented approach against the unprotected template performance and the popularly used Bloom filter template.

Biometric Cryptography is the biometric tokenization used for authentication with public-key infrastructure (PKI). In particular, biometric cryptography provides the convenience of authentication via fingerprint, face, eye, voice, palm, etc. - with none of the risks posed by having the biometrics take the form of a shared secret. With comfort, biometric verification gives more security than as it were conventional cryptography-based confirmation. Since biometric highlights of each human being are distinctive from each other, that makes it difficult to get to in an unauthorized way and it jams the privacy at the time of verification since there's no plausibility of snooping. In this manner, these days the utilization of biometric-based confirmation has expanded and utilized in versatile, portable workstation, and numerous other gadgets are utilizing biometric-based verification and biometric information stored in biometric gadgets and programmers are exceptionally shrewd they can render biometric information, can take it, and not at all like username or password biometric information can not alter, due to unique feature of biometrics. It implies, compromising biometric information will be until the end of life, and programmers can take advantage of it, so the security of biometric information is a vital issue. This paper gives some idea about cryptography methods that are capable to secure biometric data and we have presented four cryptography methods which are Biometric Encryption, Blockchain, Cancelable biometrics, and Visual cryptography. This paper also provides some challenges, issues in biometric, and future direction.

Biometric cryptosystem has emerged as a promising solution in information security. Fuzzy vault is a widely accepted scheme that binding biometric data and cryptography effectively. In vault encoding, random chaff points which are not on the polynomial are added to protect genuine points. In vault decoding, query biometric data is generated to retrieve the genuine points by measuring the distance between the registered data and query data. However, because of the within-class differences and randomness of chaff points, it is difficult to distinguish the genuine data from chaff data. This paper proposes a novel 3D fuzzy vault scheme that selects unused data from template feature vector and inserts it into the genuine points. In our scheme, every point in the vault has three dimensions, two of which could be used for distance measurement. The precision of the data matching is improved by the new inserted data and genuine points could be recognized more accurately. Experimental results based on HA-BJTU database show the better performances compared to traditional fuzzy vault.

Concerns on widespread use of biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as shown by biometric cryptosystems. In this paper, we propose a provably secure and blind biometric authentication protocol, which addresses the concerns of user's privacy, template protection, and trust issues. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the biometric to the authenticating server or vice-versa. As the protocol is based on asymmetric encryption of the biometric data, it captures the advantages of biometric authentication as well as the security of public key cryptography. The authentication protocol can run over public networks and provide nonrepudiable identity verification. The encryption also provides template protection, the ability to revoke enrolled templates, and alleviates the concerns on privacy in widespread use of biometrics. The proposed approach makes no restrictive assumptions on the biometric data and is hence applicable to multiple biometrics. Such a protocol has significant advantages over existing biometric cryptosystems, which use a biometric to secure a secret key, which in turn is used for authentication. We analyze the security of the protocol under various attack scenarios. Experimental results on four biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.

The need to protect biometric data has been well advised according to various regulations and standards. The most popular Bloom Filter-based template protection schemes for iris recognition directly depend on the keys to avoid linkability challenges. This work presents a new approach for generating the keys directly from the iris biometric data using chaotic maps and elliptic curves over finite fields. Further, we present a new template protection scheme that can directly exploit the generated keys to provide better security using a Quarter-Rounded template encoding which employs the inter-relation of bits in the neighborhood of the iriscode. The proposed approach is validated on the publicly available IITD Iris database and the comparison to the state-of-art Bloom Filter template protection. The proposed approach achieves a comparable performance with the Bloom Filter template protection while eliminating key generation overhead. The proposed template creation approach achieves Genuine Match Rate (GMR) = 99.15% at FMR= 0.01% and EER= 0.77% on the IITD Iris database, with an unlinkability score of 0 and superior revocability.

GREYC-Hashing: Combining biometrics and secret for enhancing the security of protected templates

Security of cryptographic keys is an important issue in today's unprotected world. Most reliable solution of this problem is state of art fuzzy vault which is an application of bio-cryptosystems. These systems use biometric trait for locking and unlocking the keys. During unlocking, it is necessary to do alignment of query biometric data with enrolled biometric data because of large variation in two samples of a biometric trait of a user taken over the short span of time. Complexity of alignment is increased due to absence of original fingerprint template during alignment. That's why helper data or additional information is required for alignment. There are number of techniques developed for automatic alignment of fingerprints till now. This paper gives the review of most of those methods.

We describe a simple and novel cryptographic construction that we refer to as a fuzzy vault. A player Alice may place a secret value ? in a fuzzy vault and "lock" it using a set A of elements from some public universe U. If Bob tries to "unlock" the vault using a set B of similar length, he obtains ? only if B is close to A, i.e., only if A and B overlap substantially. In constrast to previous constructions of this flavor, ours possesses the useful feature of order invariance, meaning that the ordering of A and B is immaterial to the functioning of the vault. As we show, our scheme enjoys provable security against a computationally unbounded attacker. Fuzzy vaults have potential application to the problem of protecting data in a number of real-world, error-prone environments. These include systems in which personal information serves to authenticate users for, e.g., the purposes of password recovery, and also to biometric authentication systems, in which readings are inherently noisy as a result of the refractory nature of image capture and processing.

Adding machine learning (ML) and artificial intelligence (AI) logic models to authentication is an inevitable process. In this work, we show that the combination of qualitative and quantitative verification over model created on training data may significantly reduce false access probability, even if the user's credentials ID and password are compromised. We propose three layers of authentication based on user ID and password, silent signals, and biometrical data. The system uses supervised ML to determine the user's risk level. Basic model and associate implementation performance shows that we can, with high probability, identify an intruder based on silent signals, historical data, and behavioural biometrics. The system is compositional, so further improvement by introducing more silent signals and behavioural analytics can, theoretically, eliminate false acceptance. Whenever the risk level is higher than some threshold, an additional verification is required. The threshold may increase over time and in that case, the probability of additional verification of a legitimate user decreases.