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In the last decade, mobile devices become a storage place for highly sensitive information such as authentication credentials, bank account information, etc. However, smartphones are more likely to be lost or stolen than desktops or even laptops, putting user data at a high risk of being compromised. Malware and legitimate vulnerable applications can also be a threat to data disclosure. Therefore, this work focuses on preserving the confidentiality of the information stored in mobile devices from unauthorized access and specifically in the Android platform using a fuzzy vault-based key binding algorithm. The proposed approach aims to conceal the cryptographic key with the user's fingerprint such that only the genuine user will be authorized to access the secret key. [ABSTRACT FROM AUTHOR]

The broad adoption of smartphones has superseded the desktop computers and laptops as a primary computing platform, due to mobility, constant connectivity and application diversity. Mobile devices encompass storage of extensive information including sensitive ones such as authentication credentials, pictures, videos, personal data, work information, and many more. Thus, securing data stored on mobile devices becomes a critical issue. In this review, we investigate the security of Android storage model between 2013 and 2018. Several threats are found in the literature that can be categorized as physical or software threats. Additionally, the existing solutions for each category are highlighted. Although Android provides valuable encryption systems including full disk encryption and keychain to enhance the data storage security, the encryption key, which is stored in the device, is still vulnerable to physical threats. [ABSTRACT FROM AUTHOR]

Biometric systems are developed in order to replace traditional authentication. However, protecting the stored templates is considered as one of the critical steps in designing a secure biometric system. When biometric data is compromised, unlike passwords, it can't be revoked. One methodology for biometric template protection is 'Biometric Cryptosystem'. Biometric cryptosystems benefit from both fields of cryptography and biometrics where the biometrics exclude the need to remember passwords and the cryptography provides high security levels for data. In order to, develop these systems, Fuzzy Commitment Scheme (FCS) is considered as well known approach proposed in the literature to protect the user's data and has been used in several applications. However, these biometric cryptosystems are hampered by the lack of formal security analysis to prove their security strength and effectiveness. Hence, in this paper we present several metrics to analyze the security and evaluate the weaknesses of biometric cryptosystems based on FCS. [ABSTRACT FROM AUTHOR]

The IPv4 addressing scheme, which was proposed by IETF in 1981, provides 4.3 billion unique 32-bit IP addresses but has been fully exhausted in Feb, 2011. This exhaustion of unique IP addresses poses significant challenges to the addition of new devices to the Internet as well as offering new services. Internet of Things, which provides interconnected uniquely identifiable devices in the existing Internet infrastructure, will be greatly affected by the lack of unique IP addresses. In order to connect to the existing Internet infrastructure, every new device needs a uniquely identified IP address for communication. It has been estimated that by the year 2020 more than 30 billion devices would be connected to the Internet. In order to meet the challenge of such vast requirement of unique IP addresses, the devices in IoT will have to adopt IPv6, which is the latest version of Internet Protocol. IPv6 uses 128-bit IP addresses and offers 2128 unique IP addresses. Therefore, it expands IPv4 and provides new features of end to end connections as well as new services. In this paper, the various challenges with respect to providing connectivity, security, mobility, etc., have been discussed and how IPv6 helps in meeting those challenges. [ABSTRACT FROM AUTHOR]

In this paper we present a new multi-view face recognition approach. Besides the recognition performance gain and the computation time reduction, our main objective is to deal with the variability of the face pose (multi-view) in the same class (identity). Several new methods were applied on face images to calculate our biometric templates. The Laplacian Smoothing Transform (LST) and Discriminant Analysis via Support Vectors (SVDA) have been used for the feature extraction and selection. For the classification, we have developed a new inter-communication technique using a model for the automatic pose estimation of the head in a face image. Experimental results conducted on UMIST database show that an average improvement for face recognition performance has been obtained in comparison with several multi-view face recognition techniques in the literature. Moreover, the system maintains a very acceptable running time and a high performance even in uncontrolled conditions. [ABSTRACT FROM AUTHOR]