The disruptive technology of blockchain can deliver secure solutions without the need for a central authority. In blockchain, assets that belong to a participant are controlled through the private key of an asymmetric key pair that is owned by the participant. Although, this lets blockchain network participants to have sovereignty on their assets, it comes with the responsibility of managing their own keys. Currently, there exists two major bottlenecks in managing keys; @math users don't have an efficient and secure way to store their keys, @math no efficient recovery mechanism exists in case the keys are lost. In this study, we propose secure methods to efficiently store and recover keys. For the first, we introduce an efficient encryption mechanism to securely encrypt and decrypt the private key using the owner's biometric signature. For the later, we introduce an efficient recovery mechanism using biometrics and secret sharing scheme. By applying the proposed key encryption and recovery mechanism, asset owners are able to securely store their keys on their devices and recover the keys in case they are lost.
In some studies, researchers explored applying more than one biometric traits instead of using only one biometric trait. proposed multimodal biometric system that generates a 256-bit secure cryptographic key using a combination of features from iris texture and minutiae points from the user’s finger prints @cite_22 . In the study of , they propose multimodal approach of biometric. For instance they use Iris and Fingerprint, Speech and Signature, Face and Voice etc. In the study, Iris &fingerprint modalities are used and evaluated under FAR, FRR and accuracy @cite_31 . Also, the study conducted by Yik-Herng proposes multi modal biometric systems that combine iris and fingerprint with IFO hash fusion method @cite_0 . Iris trait is unique for each individual even for identical twins. Also, false acceptance rate (FAR), the rate of invalid matches, is lower than all other biometric traits like fingerprint and face. Voice trait is a composite of both behavioral and physical biometrics. Behavioral part differentiates in time due to the factors like medical conditions and age. In contrast to token or password-based systems, biometric matching does not work well every time due to the false matching or false mismatching.
@cite_0 @cite_31 @cite_22
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Recent research in biometrics has suggested the existence of the “Biometric Menagerie” in which weak users contribute disproportionately to the error rate (FAR and FRR) of a biometric system. The aim of this work is to utilize this observation to design a multibiometric system where information is consolidated on a user-specific basis. To facilitate this, the users in a database are characterized into multiple categories and only users belonging to weak categories are required to provide additional biometric information. The contribution of this work lies in (a) the design of a selective fusion scheme where fusion is invoked only for a subset of users, and (b) evaluating the performance of such a scheme on two public datasets. Experiments on the multi-unit CASIA V3 iris database and multi-unit WVU fingerprint database indicate that selective fusion, as defined in this work, improves overall matching accuracy while potentially reducing overall computational time. This has positive implications in a large-scale system where the throughput can be substantially increased without compromising the verification accuracy of the system. Human users have a tough time remembering long cryptographic keys. Hence, researchers, for so long, have been examining ways to utilize biometric features of the user instead of a memorable password or passphrase, in an effort to generate strong and repeatable cryptographic keys. Our objective is to incorporate the volatility of the user's biometric features into the generated key, so as to make the key unguessable to an attacker lacking significant knowledge of the user's biometrics. We go one step further trying to incorporate multiple biometric modalities into cryptographic key generation so as to provide better security. In this article, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) for generation of secure cryptographic key. The proposed approach is composed of three modules namely, 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. Initially, the features, minutiae points and texture properties are extracted from the fingerprint and iris images respectively. Subsequently, the extracted features are fused together at the feature level to construct the multi-biometric template. Finally, a 256-bit secure cryptographic key is generated from the multi-biometric template. For experimentation, we have employed the fingerprint images obtained from publicly available sources and the iris images from CASIA Iris Database. The experimental results demonstrate the effectiveness of the proposed approach. Biometrics system had been widely implemented in our daily life applications. With continuous improvement in biometrics recognition performance, biometrics security hence becomes an important topic of research as biometric template protection scheme serves as a vital part of a complete biometrics system. Besides, multimodal biometrics system is introduced to improve the recognition performance, system complexity, security and applicability of nowadays biometrics applications. In this paper, we present a new approach of feature-level fusion multimodal biometrics system using indexing-first-one (IFO) hashing and integer value mapping strategy. Indexing-first-one hashing has proven survived from several major privacy attacks such as single-hash attack (SHA), attack via record multiplicity (ARM) etc. On top of that, a weighted feature level fusion approach is proposed where multiple biometrics are given different weights based on the individual recognition result which then each biometrics will contributes to the final matching result based on their respective weights. The experiment is conducted and result is validated using a multimodal fingerprint and iris database. This paper presents an approach for the personal authentication using rank-level fusion of multispectral palmprints, instead of using multiple biometric modalities and multiple matchers. The rank level fusion involving the non linear combination of hyperbolic tangent functions gives the best recognition rate for the Rank 1 obtained from two types of features, viz., sigmoid and fuzzy. The results of using rank level fusion on the publicly available multispectral palmprint database show the significant improvement in the recognition rate as compared to the individual spectral bands. Recognition rate of 99.4 from sigmoid features and that of 99.2 from fuzzy features based on Rank 1 is the outcome of the hyperbolic tangent nonlinearity.