A Novel Quantum Steganography Scheme Based on ASCII

Based on the novel enhanced quantum representation for quantum image (NEQR), a new blind quantum steganography scheme is proposed. In this scheme, an improved quantum representation of text utilizing ASCII is provided that uses two qubit sequences to store the same quantum text message. The general embedding process of the scheme is as follows: firstly, the cover image of sized 2 n × 2 n will be divided into eight blocks of sized 2 n − 2 × 2 n − 1 , and the secret quantum text of sized 2 n − 2 × 2 n − 1 is scrambled by Gray code transform method. Then, the disorder quantum text is embedded into the eight blocks of cover image employing the Gray code as a judgment condition. Meanwhile, the corresponding quantum circuits are drawn. Through the analysis of all quantum circuits, it can be concluded that the scheme has a lower complexity, that is, O(n). And the performance of the proposed scheme is analyzing in terms of simulation results of three items: visual quality, circuit complexity, and robustness

Fast image watermarking based on signum of cosine matrix

In the field of image watermarking, the singular value decomposition has good imperceptibility and robustness, but it has high complexity. It divides a host image into matrices of U, S, and V. Singular matrix S has been widely used for embedding and extracting watermark, while orthogonal matrices of U and V are used in decomposition and reconstruction. The proposed signum of cosine matrix method is carried out to eliminate the generation of the three matrices at each block and replace it with a signum of cosine matrix. The proposed signum of cosine matrix is performed faster on the decomposition and reconstruction. The image is transformed into a coefficient matrix C using the signum matrix. The C matrix values are closer to the S value of singular value decomposition which can preserve high quality of the watermarked image. The experimental results show that our method is able to produce similar imperceptibility and robustness level of the watermarked image with less computational time

Robust data protection and high efficiency for IoTs streams in the cloud

Remotely generated streaming of the Internet of Things (IoTs) data has become a vital category upon which many applications rely. Smart meters collect readings for household activities such as power and gas consumption every second - the readings are transmitted wirelessly through various channels and public hops to the operation centres. Due to the unusually large streams sizes, the operation centres are using cloud servers where various entities process the data on a real-time basis for billing and power management. It is possible that smart pipe projects (where oil pipes are continuously monitored using sensors) and collected streams are sent to the public cloud for real-time flawed detection. There are many other similar applications that can render the world a convenient place which result in climate change mitigation and transportation improvement to name a few. Despite the obvious advantages of these applications, some unique challenges arise posing some questions regarding a suitable balance between guaranteeing the streams security, such as privacy, authenticity and integrity, while not hindering the direct operations on those streams, while also handling data management issues, such as the volume of protected streams during transmission and storage. These challenges become more complicated when the streams reside on third-party cloud servers. In this thesis, a few novel techniques are introduced to address these problems. We begin by protecting the privacy and authenticity of transmitted readings without disrupting the direct operations. We propose two steganography techniques that rely on different mathematical security models. The results look promising - security: only the approved party who has the required security tokens can retrieve the hidden secret, and distortion effect with the difference between the original and protected readings that are almost at zero. This means the streams can be used in their protected form at intermediate hops or third party servers. We then improved the integrity of the transmitted protected streams which are prone to intentional or unintentional noise - we proposed a secure error detection and correction based stenographic technique. This allows legitimate recipients to (1) detect and recover any noise loss from the hidden sensitive information without privacy disclosure, and (2) remedy the received protected readings by using the corrected version of the secret hidden data. It is evident from the experiments that our technique has robust recovery capabilities (i.e. Root Mean Square (RMS) <0.01%, Bit Error Rate (BER) = 0 and PRD < 1%). To solve the issue of huge transmitted protected streams, two compression algorithms for lossless IoTs readings are introduced to ensure the volume of protected readings at intermediate hops is reduced without revealing the hidden secrets. The first uses Gaussian approximation function to represent IoTs streams in a few parameters regardless of the roughness in the signal. The second reduces the randomness of the IoTs streams into a smaller finite field by splitting to enhance repetition and avoiding the floating operations round errors issues. Under the same conditions, our both techniques were superior to existing models mathematically (i.e. the entropy was halved) and empirically (i.e. achieved ratio was 3.8:1 to 4.5:1). We were driven by the question ‘Can the size of multi-incoming compressed protected streams be re-reduced on the cloud without decompression?’ to overcome the issue of vast quantities of compressed and protected IoTs streams on the cloud. A novel lossless size reduction algorithm was introduced to prove the possibility of reducing the size of already compressed IoTs protected readings. This is successfully achieved by employing similarity measurements to classify the compressed streams into subsets in order to reduce the effect of uncorrelated compressed streams. The values of every subset was treated independently for further reduction. Both mathematical and empirical experiments proved the possibility of enhancing the entropy (i.e. almost reduced by 50%) and the resultant size reduction (i.e. up to 2:1)

A Highly Secure Quantum Communication Scheme for Blind Signature using Qubits and Qutrits

© ASEE 2014The advances in hardware speed has being rapidly increased rapidly in the recent years, which will lead to the ability to decrypt well known decryption algorithms in short time. This motivated many researchers to investigate better techniques to prevent disclosing and eavesdropping of communicated data. Quantum Cryptography is a promising solution, since it relies on the prosperities of quantum physics that ensure no change in the quantum state without the knowledge of the sender/receiver. Quantum Communication Scheme for Blind Signature with Two-Particle Entangled Quantum-Trits was proposed by Jinjing et al. [1] That scheme uses qutits during the communications and the process of the encryption is not clearly defined. In this paper we suggest a modification of Jinjing et al. protocol using qubits and qutrits during the encryption and decryption which proposed by Zhou et al. [2] The proposed algorithms enhances the efficiency of that scheme and creates a quantum cryptosystem environment to exchange the data in a secure way. During the communications, all the messages are encrypted using the the private key of the sender and a third party verifies the authenticity and the blindness of the signature