Multilevel Encryption Research Articles

High-Fidelity Measurement of Qubits Encoded in Multilevel Superconducting Circuits

Qubit measurements are central to quantum information processing. In the field of superconducting qubits, standard readout techniques are not only limited by the signal-to-noise ratio, but also by state relaxation during the measurement. In this work, we demonstrate that the limitation due to relaxation can be suppressed by using the many-level Hilbert space of superconducting circuits: in a multilevel encoding, the measurement is only corrupted when multiple errors occur. Employing this technique, we show that we can directly resolve transmon gate errors at the level of one part in $10^3.$ Extending this idea, we apply the same principles to the measurement of a logical qubit encoded in a bosonic mode and detected with a transmon ancilla, implementing a proposal by Hann et al. [Phys. Rev. A \textbf{98} 022305 (2018)]. Qubit state assignments are made based on a sequence of repeated readouts, further reducing the overall infidelity. This approach is quite general and several encodings are studied; the codewords are more distinguishable when the distance between them is increased with respect to photon loss. The tradeoff between multiple readouts and state relaxation is explored and shown to be consistent with the photon-loss model. We report a logical assignment infidelity of $5.8\times 10^{-5}$ for a Fock-based encoding and $4.2\times 10^{-3}$ for a QEC code (the $S=2,N=1$ binomial code). Our results will not only improve the fidelity of quantum information applications, but also enable more precise characterization of process or gate errors.

A Cloud Image Data Protection Algorithm with Multilevel Encryption Scheme and Automated- Selection Mechanism

In this paper, we present a cloud image data protection algorithm with a multilevel encryption scheme and automated-selection mechanism to maintain the privacy of cloud data contents. This algorithm is also useful for the protection of personal or commercial data uploaded to the cloud server for real-time applications, monitoring, and transmission. Fundamental and well-known in cryptography, the confusion–diffusion scheme, as well as an automated-selection mechanism (sliding pixel window) were selected as the main motor of the proposed algorithm to cipher images. First, a sliding pixel window is selected to expedite a two-stepped process, whether in small or big images. The confusion stage was designed to drastically change data from plain image to cipher image. The conversion of pixels from decimal to binary and their vertical and horizontal relocation were performed to help in this stage, not only by randomly moving bits, but also by changing the pixel values when they returned to their corresponding decimal values. Meanwhile, the diffusion stage was designed to destroy all possible existing patterns in the sliding pixel window after the confusion stage. Two hyperchaotic systems, together with a logistic map (multilevel scheme), produce pseudorandom numbers to separately conceal the original data of each subplain image through first- and second-level encryption processes. The two-stepped algorithm was designed to be easily implemented by practitioners. Furthermore, the experimental analysis demonstrates the effectiveness and feasibility of the proposed encryption algorithm after being tested using the benchmark “Lena” image, as well as the “Bruce Lee” image, the latter of which is completely different to the first one, statistically speaking.

Color image encryption based on vector decomposition operations and secondary image phase masks

This paper presents a method aimed at encrypting color images using vector operations and secondary image phase masks (SPMs). In this encryption, the optical coherent superposition principle is used to separate and overlay the original image into two phase plates on the three channels R, G and B, respectively. Then the double random phase encoding in the Fresnel domain is performed using the SPMs to encrypt one of the phase plates. Finally, the Kronecker product of two random matrices is directly perform randomization on the encoded image, achieving multilevel encryption of color images. Computer simulations are presented to demonstrate its performance, and the feasibility and security of the proposed system are analyzed as well.

An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography

Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.

Embedding Digital Signature Using Encrypted-Hashing for Protection of DSP Cores in CE

Reusable intellectual property (IP) cores from signal processing and multimedia form an integral part of consumer electronics (CE) systems. However, owing to the value it represents, it needs protection against important threats, such as piracy and illegal claim of ownership. This paper presents a novel multi-level encoding and encrypted-hash-based digital signature for protection of complex reusable IP core used in CE systems. For the first time in the literature, a digital signature-based IP core protection is proposed for digital signal processing (DSP) kernels. The proposed methodology is capable of encoding a DSP kernel application, followed by creating a digest of the encoded application to finally form a digital signature using RSA, which is subsequently implanted into the same design during architectural synthesis. The proposed approach on DSP benchmarks is capable of achieving higher robustness in terms of lower probability of coincidence - indicating stronger proof of authorship. The proposed approach achieves stronger robustness (on average by ~24.8%) as well as requires lesser storage hardware (on average reduction of ~14%) compared to similar prior work.

Improvement of Image Steganography Scheme Based on LSB Value with Two Control Random Parameters and Multi-level Encryption

Recently, steganography has played an important part in the field of communication, especially in image steganography. The major points of image steganography are the image quality (imperceptibility) of the stego image and the security of the system towards stopping the recoverability of the secret data. A new steganography scheme based on two control random parameters and multi-level encryption can address the security challenge while the P_Even/P_Odd classification can ensure the imperceptibility of the stego image. The objectives of study to increase the security and PSNR of the image by using the Huffman coding technique to compress the secret data prior to embedding; this will also ensure an increase in the payload capacity. The proposed scheme takes effect after encrypting and compressing the secret data. It is deployed when matching the secret bits with the LSB during embedding to determine 0 (P_Even) and 1 (P_Odd) while classifying the secret message to track and map each bit in the stego image. The results showed the embedding of the secret message based on P_Even/P_Odd with two control random parameters and multi-level encryption to improve the steganography.

PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing

The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become insufficient or slow. Moreover, the required signal-to-noise ratio of the received signal can be challenging, especially when adopting modulation formats with multi-level encoding. Lately, photonic reservoir computing (RC) - a hardware machine learning technique with recurrent connectivity - has been proposed as a post-processing tool that deals with deterministic distortions from fiber transmission. Here we show that RC post-processing is remarkably efficient for multilevel encoding and for the use of very high launched optical peak power for fiber transmission up to 14dBm. Higher power levels provide the desired high signal-to-noise ratio (SNR) values at the receiver end, at the expense of a complex nonlinear transformation of the transmission signal. Our demonstration evaluates a direct fiber communication link with 4-level pulse amplitude modulation (PAM-4) encoding and direct detection, without including optical amplification, dispersion compensation, pulse shaping or other digital signal processing (DSP) techniques. By applying RC post-processing on the distorted signal, we numerically estimate fiber transmission distances of 27km at 56Gb/s and of 5.5 km at 112Gb/s data encoding rates, while fulfilling the hard-decision forward error correction (HD-FEC) bit-error-rate (BER) limit for data recovery. In an experimental equivalent demonstration of our photonic reservoir, the achieved distances are 21km and 4.6km respectively.

Multi-level encryption algorithm for user-related information across social networks

AbstractThe traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps.

Multilevel Encryption of Messages for Secured Communication

The security of information passed through the communication channel has become a major concern. Encoding of the messages before transmitting through the channel is most vital. We propose a three level encryption (encoding) using musical notes in traditional Indian and Western system as well as the American Standard Code for Information Interchange (ASCII). A unique method for the decryption is also mentioned in the paper.

A Quality-Configurable Approximate Serial Bus for Energy-Efficient Sensory Data Transfer

Mobile, wearable, and implantable devices integrate an increasing number and variety of sensors, such as microphones, image sensors, and accelerometers. These devices spend substantial amounts of time reading the sensors within them, incurring significant data transfer from the sensor to the processor. The high capacitance of sensor-to-processor interconnects, high data rates of modern sensors and frequent usage of applications that use sensors result in significant energy dissipation for sensory data transfer. The contribution of sensing to power consumption is only expected to increase as more and more high-performance sensors are embedded in a single device. To address this challenge, we propose AxSerBus, a quality-configurable approximate serial bus that exploits the locality of sensory data and the error resiliency of sensing applications to reduce energy dissipation. AxSerBus significantly reduces signal transitions by encoding the differences of sensory data in three encoding modes, depending on the magnitude of the differences: very small differences are zeroed out, incurring no energy dissipation; intermediate differences are encoded using special low-transition count patterns; and for high differences, the absolute value (not the difference) of the data is transmitted. Compared with previous schemes, the proposed multi-level encoding results in more data being encoded using low-energy patterns. In addition, in the intermediate difference encoding mode, the differences are encoded in an approximate manner, and the approximation bounds are proportional to the magnitude of the differences. Since small differences are more frequent than large differences in sensory data, the proposed encoding scheme also minimizes quality degradation. We demonstrate that AxSerBus achieves superior energy vs. quality trade-offs compared with previous schemes. We also present a low-overhead heuristic scheme for dynamic quality configuration. In an image processing application, AxSerBus reduces the signal transitions up to 90% with minor image quality degradation. In the context of an optical character recognition (OCR) application, AxSerBus achieves 79.4% reduction in dynamic power dissipation, while maintaining accuracy above 95%.

3-bit writing of information in nanoporous glass by a single sub-microsecond burst of femtosecond pulses.

We have found that a single sub-microsecond burst of femtosecond laser pulses produces a sub-micrometer cavity possessing the homogeneous birefringence with slow-axis orientation perpendicular to polarization of the laser beam in high-silicate nanoporous glass. Retardance and the root mean square of slow-axis orientation are investigated in dependence on the energy and the number of pulses in the burst. A burst of just three pulses with 98ns inter-pulse intervals has been shown to induce homogeneous birefringence, and a burst of four pulses has provided birefringence with retardance of 35nm, which is sufficient for reliable readout of the information recorded with multilevel encoding in slow-axis orientation. A text file has been recorded and read out in an array of birefringent cavities, each carrying 3 bits of information. The sub-microsecond burst of femtosecond pulses paves the way for a multiple increase of the rate of digital information recording with multilevel encoding in glass.

Securing robot communication using packet encryption distribution

With the widespread deployment of mobile networks, wireless communications are increasingly preferred both by ordinary users and researchers working on mobile applications communication. Securing a wireless connection is essential since an eavesdropper might interrupt a connection to collect data illegitimately, or interfere with the transmission by adding misleading data, eliminating critical information, or altering transmitted messages. With the widespread deployment of mobile networks, wireless communications are increasingly important to both ordinary users and researchers. Securing a wireless connection is essential since an eavesdropper might interrupt a connection to collect data, or interfere with the transmission. Saad Al-Azzam, Ahmad Sharieh and Azzam Sleit of the University of Jordan and Nedaa Al-Azzam of Al-Dammam University propose an algorithm that secures both the message itself by employing a multi-level encryption technique and secures the transmission by broadcasting it over multiple interfaces.

Data Provenance With Retention of Reference Relations

With the development of data transactions, data security issues have become increasingly important. For example, the copyright authentication and provenance of data have become the primary requirements for data security defense mechanisms. For this purpose, this paper proposes a data provenance system with retention of reference relations (called RRDP), which can enhance the security of data service in the process of publishing and transmission. The system model for data provenance with retention of reference relations adds virtual primary keys using reference relations between data tables. Traditional provenance algorithms have limitations on data types. This model has no such limitations. Added primary key is auto-incrementing integer number. Multi-level encryption is performed on the data watermarking to ensure the secure distribution of data. The experimental results show that the data provenance system with retention of reference relations has good accuracy and robustness of the provenance about common database attacks.

Multi-Level Encryption Framework

Multi-level encryption approaches are becoming more popular as they combine the strength of multiple basic/traditional approaches into a complex one. Many multi-level encryption approaches have been introduced for different systems, like Internet of Things, sensor networks, big data, and the web. The main obstacles in building such approaches are to have a secure as well as a computationally efficient multi-level encryption approach. In this paper, we propose a computationally efficient multi-level encryption framework that combines the strength of symmetric, the encryption algorithm AES (Advance Encryption Standard), Feistel network, Genetic Algorithm’s Crossover and Mutation techniques, and HMAC. The framework was evaluated and compared to a set of benchmark symmetric encryption algorithms, such as RC5, DES, and 3-DES. The evaluation was carried out on an identical platform and the algorithms were compared using the throughput and running time performance metrics and Avalanche effect security metric. The results show that the proposed framework can achieve the highest throughput and the lowest running time compared to the considered benchmarked symmetric encryption algorithms and passes the avalanche effect criterion.

ENHANCING THE SECURITY OF IOT DATA USING MULTILEVEL ENCRYPTION

Internet of Things (IoT) plays a vital role in the field of Information Technology, Industries and Healthcare etc. As data in IoT applications will be related to the physical realm, ensuring data security is a primary constraint for many cases. Because in the IoT context not only users, but also authorized objects may access data. Security represents a critical component for enabling the widespread adoption of IoT technologies and applications. Therefore this paper proposes a multilevel encryption technique to enhance the security of the IoT data. In this approach the data sensed from the IoT devices are encrypted in the gateway using Merkle-Hellman encryption and Elliptic Curve Cryptography (ECC) to ensure the security of the data.

A multithreaded programming approach for multimedia big data: encryption system

Multimedia is currently seen to dominate the internet network and the mobile network traffic; hence, it is seen as the largest Big data. Generally, the symmetric encryption algorithms are applied to the ‘big multimedia data’; however; these algorithms are thought as very slow. In our study, we developed and designed a resource-efficient encryption algorithm system which applies the multithreaded programming process for the encryption of the big multimedia data. This proposed system describes a multi-level encryption model which uses the Feistel Encryption Scheme, genetic algorithms and the Advanced Encryption Standard (AES). Our system has been assessed for actual medical-based big multimedia data and compared to the benchmarked encryption algorithms like the RC6, MARS, 3-DES, DES, and Blowfish with regards to the computational run time and its throughput for the encryption and decryption procedures. In addition, the multithreaded programming approach is adopted to implement the proposed encryption system in order to enhace the system effeciencey and porfermance. Furthermore, we also compared our system with its sequential version for showing its resource efficiency. Our results indicated that our system had the least run time and a higher throughput for the encryption and decryption processes in comparison to the already existing standard encryption algorithms. Also, our system could improve the computation run time by approximately 75% and its throughput was also increased by 4-times in comparison to its sequential version. For fulfilling the security objectives, our algorithm showed a better Avalanche Effect in comparison to the existing algorithms and therefore, could be included in any encryption/decryption process of a big plain multimedia data.

Generating random grid-based visual secret sharing with multi-level encoding

Most visual secret sharing (VSS) schemes are employed for the secret sharing of binary secret images. In these schemes, the share appears as a noise-like image, such that no information about the secret image can be obtained from an individual share. The recovered secret message can only be decrypted by the human eye following rendering using two different light transmissions. However, these schemes do not apply well to gray-level secret images because the halftone region of gray-level secret images cannot be adequately recovered. In this paper, we propose a VSS scheme with multi-level encoding based on a generalized random grid, which can retrieve the secret in a multi-level secret image with a high visual quality using the XOR operant during decryption. The proposed method produces a share of the same size as the original secret image without pixel expansion and does not require a codebook in the encoding process.

Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection

Objectives: Nowadays cloud is becoming the major research area because of the security threats against cloud. According to the reports of CSA (cloud security alliance), much more security issues are related to authentication and improper data security etc. Methods/Statistical Analysis: Such problems can be neutralized by adopting biometric authentication mechanism in a novel way by providing protection in every direction since cloud is a medium where much more valuable computational data have been stored and transferred. Findings: In our approach, biometric authentication has been given a new dimension for the different level of security. And the public key and private key encryptions have been used in combination where data needs to be protected. By merging these biometric authentications along with multi-level encryption will give the enhanced security over the cloud and increase the confidentiality to use the cloud among cloud consumers as well as cloud providers. Applications/Improvements: Implementation of such methodology has given tremendous results over the security issues, authentication issues.

Reversible data hiding in encrypted images based on multi-level encryption and block histogram modification

In recent years there has been significant interest in reversible data hiding, and also in particular, reversible data hiding in encrypted images (RDH-EI). This means that additional data can be embedded into a previously encrypted image with no knowledge of the original image content. According to the held keys, legal receivers can get the embedded data or an image very similar to the original one, or, both the embedded data and an image exactly as the original one. In this paper, we propose and evaluate a RDH-EI framework. Firstly, we propose a multi-level encryption (MLE) scheme using both Josephus traversal based multi-granular encryption and a stream cipher. To reduce the quantity of side information required to embed into images together with additional data, we also present a block histogram modification (BHM) approach with self-hidden peak pixels to perform reversible data embedding and a location map marking scheme to perform histogram contraction and recovery. The experimental results demonstrate that, in comparison with other similar methods, the proposed framework achieves improvements in terms of the embedding payload, the decrypted image quality and the accuracy of image restoration.

CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method

Information hiding is an active area of research where secret information is embedded in innocent-looking carriers such as images and videos for hiding its existence while maintaining their visual quality. Researchers have presented various image steganographic techniques since the last decade, focusing on payload and image quality. However, there is a trade-off between these two metrics and keeping a better balance between them is still a challenging issue. In addition, the existing methods fail to achieve better security due to direct embedding of secret data inside images without encryption consideration, making data extraction relatively easy for adversaries. Therefore, in this work, we propose a secure image steganographic framework based on stego key-directed adaptive least significant bit (SKA-LSB) substitution method and multi-level cryptography. In the proposed scheme, stego key is encrypted using a two-level encryption algorithm (TLEA); secret data is encrypted using a multi-level encryption algorithm (MLEA), and the encrypted information is then embedded in the host image using an adaptive LSB substitution method, depending on secret key, red channel, MLEA, and sensitive contents. The quantitative and qualitative experimental results indicate that the proposed framework maintains a better balance between image quality and security, achieving a reasonable payload with relatively less computational complexity, which confirms its effectiveness compared to other state-of-the-art techniques.