User's Biometric Features Research Articles

A secured wireless charging scheme with data transmission

This research task proposes a magnetic resonant coupling wireless power transfer(MRC-WPT) scheme for electric vehicles, which realizes secure data communication between the wireless charging station and the electric vehicle using binary phase shift-keying (BPSK) data transmission, thereby waving any radio frequency(RF) communication links. The secondary side is designed to accept power, while it transmits data back to the primary side simultaneously. The BPSK design ensures data transmission robustness to against disturbance interference. The data are encrypted by the user's biometric features for information security. The magnetic resonance performs better than that of the traditional magnetic induction in the power transmission efficiency against coil misalignments. With the Internet of Thing(IOT) architecture, the encrypted data can be stored and retrieved via the cloud server.

Privacy preservation and security challenges: a new frontier multimodal machine learning research

Multimodal machine learning is a vibrant multi-disciplinary field and achieved much attention due to its wide range of applications. A research problem is multimodal, for the impact of privacy preservation. It shields sensitive data in the cloud by using a single modality-based privacy system. The user's biometric features are always stored in the database, primarily present in the cloud server to validate the user and his access. This facet provides a beneficial quality but at the same time has raised crucial affairs in security and privacy of biometric feature set. The main concern is to manage and stop the privacy breaches in clouds. The article discusses the detailed analysis of security schemes with a multimodal-based learning framework over sensitive data and systems at both ends. The article also accentuates frameworks and schemes that may apply in various applications to ensure privacy preservation of individuals and data security by multimodal algorithms.

IMPLEMENTATION OF PRIVACY PRESERVING AND DYNAMIC SEARCHING MECHANISM WITH BIOMETRIC AUTHENTICATION IN CLOUD STORAGE

In the recent times, cloud storage tends to be a primary storage means for external data. Cloud defense of the data against attacks is the main challenge. Private or semi-private information growth has rapidly expanded over the information network; privacy safeguards have failed to address the search mechanisms. In the field of information networks, privacy protection is an important factor in carrying out various data mining operations with encrypted data stored in different storage systems. A tolerance and protection against data corruption mechanism should be developed which is difficult to achieve. Furthermore, as there is no adequate audit mechanism, the integrity of the stored data become questionable. In addition to this, the user authentication is another challenge. The current solution provides only a remote audit mechanism. It requires data owners to always remain online so that the auditing process is manually handled, which is sometimes unworkable. In this paper, we propose a new, regenerative, public audit methodology accompanied by third-party audits. The existing data search system provides one solution that can be used to maintain the confidentiality of indexing. Documents are stored on a private server in plain word form, which compromise the protection of privacy. So that this system is improved to make the document more secure and efficient, we first store the documents in encrypted form on server, and use the Key Distribution Center (KDC). To generate keys the KDC uses the user's biometric feature. In order to improve the search experience, we also implement TF-IDF, which provides an efficient evaluation of the results. Lastly, we carry out comprehensive data set experiments to evaluate our proposed system performance. Experimental results demonstrate that in terms of safeguarding the privacy, efficient and safe search for encrypted distributed documents the proposed system is better than existing. The methodology suggested also includes an auditing mechanism by third parties to ensure data integrity.

A Biometric-Secured Neighborhood Vector Relational Coefficient Framework for Social Network Communication

Multimodal Biometric has been widely researched to improve social network user communication. Appropriate modeling of authentication and access control is highly required to perform precise system analysis in a social network framework. In this work, a Multimodal Biometric Secured (MBSec) Social Network User Communication framework to provide user authentic social communication in a safe environment is investigated. The multimodal user biometric features (i.e. face and fingerprint) with neighborhood feature pixel dominancy stored in a spatial vector are gene encoded to generate biometric identity keys. The authenticity of user is validated by decoding gene encoded biometric features with biometric identity keys. User relational coefficient is calculated for users, using prior knowledge instances shared between different social users. The social network user’s relational coefficient is sequenced in a matrix to identify the occurrence of authentic user relational social communication. Experiments were conducted using face and fingerprint images collected from BioSecure dataset. Our results demonstrate that the proposed social network user communication framework is able to significantly improve the true positive rate of authenticated users, compared to conventional FR approaches that only make use of a single model. Further, we demonstrate that our MBSec framework has a low social network authentication time and access control time.

Cryptographic Key Generation from Multiple Biometric Modalities: Fusing Minutiae with Iris Feature

Human users find difficult to remember long cryptographic keys. Therefore, researchers, for a long time period, have been investigating ways to use biometric features of the user rather than memorable password or passphrase, in an attempt to produce tough and repeatable cryptographic keys. Our goal is to integrate the volatility of the user's biometric features into the generated key, so as to construct the key unpredictable to a hacker who is deficient of important knowledge about the user's biometrics. In our earlier research, we have incorporated multiple biometric modalities into the cryptographic key generation to provide better security. In this paper, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) for generating a secure cryptographic key, where the security is further enhanced with the difficulty of factoring large numbers. At first, the features, minutiae points and texture properties are extracted from the fingerprint and iris images respectively. Then, the extracted features are fused at the feature level to obtain the multi-biometric template. Finally, a multi-biometric template is used for generating a 256-bit cryptographic key. For experimentation, we have used the fingerprint images obtained from publicly available sources and the iris images from CASIA Iris Database. The experimental results have showed that the generated 256-bit cryptographic key is capable of providing better user authentication and better security.