Abstract With the advancement of communication technology, a large number of data are constantly transmitted through the internet for various purposes, which are prone to be illegally accessed by third parties. Therefore, securing such data is crucial to protect the transmitted information from falling into the wrong hands. Among data protection schemes, Secret Image Sharing is one of the most popular methods. It protects critical messages or data by embedding them in an image and sharing it with some users. Furthermore, it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method. Despite its advantages, this method may produce noise, making the resulting stego file much different from its cover. Moreover, the size of private data that can be embedded is limited. This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data. To recover the cover image, the SS method based on the Chinese remainder theorem is used. The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.

Abstract Hiding secret data in digital multimedia has been essential to protect the data. Nevertheless, attackers with a steganalysis technique may break them. Existing steganalysis methods have good results with conventional Machine Learning (ML) techniques; however, the introduction of Convolutional Neural Network (CNN), a deep learning paradigm, achieved better performance over the previously proposed ML-based techniques. Though the existing CNN-based approaches yield good results, they present performance issues in classification accuracy and stability in the network training phase. This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images. The proposed method comprises three phases: pre-processing, feature extraction, and classification. Firstly, in the pre-processing phase, we use spatial rich model filters to enhance the noise within images altered by data hiding; secondly, in the feature extraction phase, we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features; and finally, in the classification, we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation, followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function. The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2% with reduced training time up to 30.81%.

The proliferations of mobile crowdsensing (MCS) services using smartphones with various embedded sensors have enabled people to involve in public sensing activities for various applications. Nevertheless for more complex applications such as smart cities, traffic monitoring, disaster prevention more variety of sensors are required. Hence combination of smartphone and Internet of Things (IoT) devices to form crowdsensing cluster will give great advantages. This paper proposes design and implementation of mobile crowdsensing framework to support integration of smartphones and IoT devices as a mobile crowdsensing cluster. The prototype of the proposed framework has been successfully implemented and tested using real devices and infrastructure with promising results.

Abstract With fast evolution of mobile devices and mobile network, the need of protecting user sensitive information locally and performing secure user authentication remotely become evermore increasing. Bio‐cryptography is emerging as a powerful solution which can combine the advantages of conventional cryptography and biometric security. In this paper, we present an efficient bio‐cryptographic security protocol designed for client/server authentication in current mobile computing environment, with a reasonable assumption that server is secure. In this protocol, fingerprint biometric is used in user verification, protected by a computationally efficient Public Key Infrastructure (PKI) scheme, Elliptic Curve Cryptography (ECC). The genuine fingerprint information is hidden in the feature vault which is the mixture of genuine and chaff features. Fingerprint features are not only used for biometric verification but also for cryptographic key generation. Our security analysis shows that the proposed protocol can provide a secure and trustworthy authentication of remote mobile users over insecure network. Experimental results on public domain database show an acceptable verification performance. We also tested the computational costs and efficiency of our protocol on the CLDC emulator using Java ME (previous J2ME) programming technology. The simulation results prove that the proposed protocol suits current mobile environment. Copyright © 2010 John Wiley & Sons, Ltd.

Biometrie authentication systems can address the problems of genuine user verification and usability suffered by the conventional cryptosystems based on the password and token. However, as biometrics is not replaceable, its privacy has become a concern. Also, if the same biometrics is used in multiple applications, it will be vulnerable to potential cross-matching attacks. In this paper, a projection line method is proposed for cancelable fingerprint template design. The main advantage of this method is its simplicity. Because there exist many possibilities projecting minutia into the projection line controlled by the whole range of possible projection line properties, the proposed approach offers non-invertible transformation and also possess good revocability property.

The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited onboard resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional solution is to either use symmetric encryption algorithms or hybrid symmetric and asymmetric algorithms at the expense of weaker security strength. In our proposed mobile voting scheme, the users' votes are secured by using the elliptic curve cryptography (ECC) algorithm. ECC is chosen as it has smaller key size than other public key cryptographies; and its homomorphic encryption property which is able to keep users' anonymity. We evaluate the method of ECDH with AES and ECC in comparison with our proposed method. The experiment demonstrates that our proposed ECC based scheme can outperform the traditional hybrid symmetric and asymmetric cryptographic scheme in the context of mobile evoting environment.