Encryption Framework Research Articles

Detect-TPE: A New Framework for Ideal Thumbnail-Preserving Encryption via Face Detection

Detect-TPE: A New Framework for Ideal Thumbnail-Preserving Encryption via Face Detection

OPTIMIZED CLOUD SECURITY ECC-ENHANCED HOMOMORPHIC PAILLIER RE-ENCRYPTION

In the dynamic domain of cloud computing, ensuring data security is of utmost importance. Conventional encryption techniques, while providing a high level of security, introduce substantial computational burdens, rendering them impractical for environments with limited resources. In response to this predicament, our study introduces a novel lightweight encryption framework that amalgamates Elliptic Curve Cryptography (ECC) with Homomorphic Paillier Re-Encryption, thereby reinforcing the security of data within cloud infrastructures.Our methodology exploits the inherent advantages of ECC, notably its ability to maintain stringent security measures with relatively smaller key dimensions, thereby optimizing efficiency without sacrificing the level of security. The integration of ECC with Homomorphic Paillier Encryption facilitates the execution of secure computations on ciphered data, maintaining user privacy while permitting the cloud to perform meaningful data operations. The re-encryption feature of our scheme ensures the secure mobility and modification of data sans decryption, thus augmenting security measures and operational flexibility.The proposed encryption paradigm has been validated through rigorous theoretical scrutiny and empirical implementation, revealing marked enhancements in both computational efficiency and security measures when juxtaposed with established encryption techniques. The empirical evidence suggests that the streamlined nature of our encryption scheme renders it exceptionally compatible with real-world cloud applications, particularly in scenarios where the optimization of resources is imperative.This work contributes to the field of cloud data security by providing a scalable, efficient, and secure encryption solution, paving the way for more secure and practical cloud computing applications.

An efficient confidentiality scheme based on quadratic chaotic map and Fibonacci sequence

<p>In today's rapidly evolving digital landscape, secure data transmission and exchange are crucial for protecting sensitive information across personal, financial, and global infrastructures. Traditional cryptographic algorithms like RSA and AES face increasing challenges due to the rise of quantum computing and enhanced computational power, necessitating innovative approaches for data security. We explored a novel encryption scheme leveraging the quadratic chaotic map (QCM) integrated with the Fibonacci sequence, addressing key sensitivity, periodicity, and computational efficiency. By employing chaotic systems' inherent unpredictability and sensitivity to initial conditions, the proposed method generates highly secure and unpredictable ciphers suitable for text and image encryption. We incorporated a combined sequence from the Fibonacci sequence and QCM, providing enhanced complexity and security. Comprehensive experimental analyses, including noise and occlusion attack simulations, demonstrate the scheme's robustness, resilience, and practicality. The results indicated that the proposed encryption framework offers a secure, efficient, and adaptable solution for digital data protection against modern computational threats.</p>

Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National Institute of Science and Technology). The analytical results illustrate the importance of the proposed framework by achieving better security performance and outperforming the other existing models. Lastly, the proposed framework has more potential than the other existing frameworks and finds its place in real-time healthcare systems, but it needs to be improvised for real-time datasets.

Autoencoder-based joint image compression and encryption

Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency.

Robust multifunctional watermarking system based zero-watermarking and polar harmonic Fourier moments

The pseudo-randomness exhibited by chaotic systems has proven valuable not only in plaintext information encryption of images with varying dimensions but also as a key signal for protecting image copyright information. Image copyright protection is a crucial part of image information protection. Currently, digital watermarking technology is predominantly employed for copyright protection of digital images. However, most of the existing watermarking technologies primarily focus on the improvement of watermarking performance, neglecting the security of the watermarking system itself. Drawing inspiration from the Latin square rule, this paper proposes a novel hyperchaotic mapping modulation framework, which is employed to construct a new 3D hyperchaotic mapping and enhance the security of the fragile watermark and the zero-watermark generation process. Additionally, it is integrated into the classical image encryption framework of scrambling-diffusion to safeguard meaningful copyright information. Moreover, a functional watermarking system is designed based on the RSA asymmetric encryption algorithm, least significant bit (LSB), and polar harmonic Fourier moments (PHFMs). The proposed system enables identity verification, tampering location, and copyright authentication simultaneously. During the generation of zero watermarks, the binarization threshold of the PHFMs information matrix is determined by the method of maximum variance. The experimental results demonstrate that this scheme achieves a high level of security, better robustness to both conventional and geometric attacks, and precise localization for different types of tampering attacks.

Orbital angular momentum multiplexing holography based on multiple polarization channel metasurface

Abstract As a high-degree-of-freedom approach to manipulate the electromagnetic wave, metasurfaces are widely used in high-capacity information technology. Extensive investigations have explored multiplexing techniques using polarization, incident angle, wavelength, and infinite-dimensional multiplexing through Orbital Angular Momentum (OAM). However, due to the limited spatial resolution and array size of the metasurface, the number of multiplexing channels that can be actually realized is limited. Therefore, research on the combination of OAM multiplexing and polarization degrees of freedom is of great significance. Here, we propose and experimentally demonstrate a metasurface holography multiplexing scheme based on multiple polarization channels combined with OAM. Taking advantage of the orthogonal independence of spin angular momentum and orbital angular momentum, multiple OAM multiplexing holograms are constructed in multiple different spin-polarization channels. Utilizing the well-established compatibility between OAM multiplexing and polarization multiplexing, we successfully integrated two multiplane holograms and 15 OAM multiplexing holograms on a single metasurface. Subsequently, we introduced an optical nested encryption framework designed for parallel communication. This work facilitates high-capacity and high-security holography by employing multiplexing metasurfaces, thereby providing innovative design concepts for optical communication, information encryption, and related domains.

3D mesh encryption with differentiated visual effect and high efficiency based on chaotic system

With the increasing value of 3D mesh in the economic, social, commercial, and defense sectors, its security is becoming increasingly important. Meanwhile, the 3D mesh may be distributed to people with different security levels, for whom the mesh should present a differentiated visual effect. Recently, some researchers have proposed one encrypted scheme that can meet the above requirements; however, the scheme is complex and time costly. Motivated by this, we analyze the structure of the 3D mesh and the features of the scheme with differentiated visual effects. A novel encryption framework (XOR-permutation) combined with the chaotic system is constructed to encrypt 3D meshes, which achieves the differentiated visual effects and reduces the time cost. Experiments show that the proposed scheme is enough security and the time cost with the previous work is more than ten or tens of times higher than the proposed scheme.

A Study on Customer Satisfaction and Perception of Online Banking Services in Salem District

In our bustling lives, we are in a test of skill and endurance consistently. Everybody really must attempt to carve out ways of saving opportunity. One of the innovative headways that empowers clients to go through with their monetary exchanges from a retail or mechanized bank, credit association, or other monetary foundation on a protected stage is web based banking. Internet banking, likewise alluded to as electronic banking, is the advanced appropriation of crisp banking services and products to clients straightforwardly through electronic, intuitive contact organizations. It gives clients many advantages and qualities over customary banking. Internet banking is a speedy and simple method for getting cell phone admittance to your bank whenever. Web based financial purposes the web for of circulating monetary exchanges. With regards to getting everyday things done, bank moves are a relic of past times thanks to internet banking services. One of the most popular services presented by banks is internet banking, which makes it a truly dependable stage. Banks regularly utilize encryption frameworks to ensure the security of all client information and the shortfall of information breaks. Eventually, it shields against online extortion and record control. The pace of web access, the new web based financial elements, and the recurrence of internet banking utilize all influence how rapidly internet banking creates in every country. At the point when the tasks of banks and monetary organizations are joined with data innovation, web based banking is made in the banking sector. Hence, the present paper has been aimed to give an analytical view on customer satisfaction and preference on online banking services in Salem district.

A unified construction of weightwise perfectly balanced Boolean functions

At Eurocrypt 2016, Méaux et al. presented FLIP, a new family of stream ciphers that aimed to enhance the efficiency of homomorphic encryption frameworks. Motivated by FLIP, recent research has focused on the study of Boolean functions with good cryptographic properties when restricted to subsets of the space F2n. If an n-variable Boolean function has the property of balancedness when restricted to each set of vectors with fixed Hamming weight between 1 and n−1, it is a weightwise perfectly balanced (WPB) Boolean function. In the literature, a few algebraic constructions of WPB functions are known, in which there are some constructions that use iterative method based on functions with low degrees of 1, 2, or 4. In this paper, we generalize the iterative method and contribute a unified construction of WPB functions based on functions with algebraic degrees that can be any power of 2. For any given positive integer d not larger than m, we first provide a class of 2m-variable Boolean functions with a degree of 2d−1. Utilizing these functions, we then present a construction of 2m-variable WPB functions gm;d. In particular, gm;d includes four former classes of WPB functions as special cases when d=1,2,3,m. When d takes other integer values, gm;d has never appeared before. In addition, we prove the algebraic degree of the constructed WPB functions and compare the weightwise nonlinearity of WPB functions known so far in 8 and 16 variables.

Optical encryption using a sparse-data-driven framework

The issue of integration of optical cryptosystems and deep learning (DL) algorithms have attracted a great deal of attention recently. When neural network models pre-trained by a series of known plaintext-ciphertext pairs is utilized for decryption, however, all the physical secret keys become useless for most of reported optical encryption systems, and the security of those systems consequently relies too heavily on the network models. In this paper, we propose a sparse-data-driven framework for speckle-based optical encryption, which add an extra layer of security to DL-assisted optical cryptosystems. Two networks are trained in the encryption, one is associated with secret keys and the other is driven by sparse data. The framework we build can provide flexibility by allowing the cryptosystem to adapt the generation of cyphertext to a changeable strategy. The effectiveness and security of the proposed framework for speckle-based optical encryption is verified with different experimental results. We hope the considerations presented in this work could promote the application of DL in different types of optical cryptosystems.

A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment. In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases.

Audio Encryption Framework Using the Laplace Transformation

Digital information, especially multimedia and its applications, has grown exponentially in recent years. It is important to strengthen sophisticated encryption algorithms due to the security needs of these innovative systems. The security of real-time audio applications is ensured in the present study through a framework for encryption. The design framework protects the confidentiality and integrity of voice communications by encrypting audio applications. A modern method of securing communication and protecting data is cryptography. Using cryptography is one of the most important techniques for protecting data and ensuring the security of messaging. The main purpose of this paper is to present a novel encryption scheme that can be used in real-time audio applications. We encrypt the sound using a combination of an infinite series of hyperbolic functions and the Laplace transform, and then decrypt it using the inverse Laplace transform. The modular arithmetic rules are used to generate the key for the coefficients acquired from the transformation. There is no loss of data or noise in the decryption sound. We also put several sound examples to the test

Metaheuristic-supported image encryption framework based on binary search tree and DNA encoding

Metaheuristic-supported image encryption framework based on binary search tree and DNA encoding

Is Safety Always Going to be More Important Than Privacy?

Lately Elon Musk published headlines news that has been focused on his private jet and its whereabouts, leading the billionaire to announce he is taking legal action against a 20-year-old who has been tracking his travels. The US Federal Aviation Administration is proposing an upgrade to air transportation that will fundamentally overhaul the current, aging system. A key component, the automatic dependent surveillance broadcast (ADS-B) system, will enhance air traffic monitoring and control by requiring aircraft to continually broadcast position, identity and velocity via unencrypted data links to ground stations. Although ADS-B may enhance air traffic safety and support the increase in traffic demands, open broadcast of clear aircraft data points raise serious security concerns. The ability to encrypt ADS-B message transactions would afford protection to ensure that the confidentiality of aircraft data is not compromised. The implementation of an encryption framework for a large, distributed and dynamic system, however, is nontrivial. This paper discusses the ADS-B as a tool — the way that it is beneficial and the way it could be exploited.

A Modular Encryption Framework in Cloud and Mobile Environments for Cybersecurity Solutions in Health Information

The rapidly evolving landscape of healthcare information systems, ensuring the security and privacy of sensitive health data is paramount. This abstract introduces an encryption framework designed for the unique challenges posed by cloud and mobile environments, offering robust cybersecurity solutions for health information. The framework integrates cutting-edge encryption technologies tailored to the specific requirements of both cloud-based storage and mobile devices, addressing the dynamic nature of healthcare data access. In cloud environments, the framework employs advanced encryption algorithms to protect data at rest, in transit, and during processing. Additionally, it leverages key management strategies to ensure secure access control and mitigate unauthorized breaches. For mobile platforms, the framework focuses on end-to-end encryption, safeguarding health information across diverse devices while considering the inherent vulnerabilities of mobile networks. Furthermore, it incorporates secure authentication mechanisms to fortify access points and prevent unauthorized entry. This comprehensive Modular encryption framework represents a proactive approach to healthcare cybersecurity, acknowledging the critical importance of safeguarding patient data. By seamlessly integrating encryption measures into both cloud and mobile environments, it provides a robust solution to the multifaceted challenges in health information security, fostering a resilient and protected digital ecosystem for healthcare providers and patients alike.

Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

As an important application of image encryption, digital medical image encryption plays an important role in the field of medical health and privacy protection. This paper put forwards a fully chaotic and strongly plaintext associated image encryption framework based on an improved chaotic system, block rotation and DNA computing. The algorithm generates multiple chaotic sequences by using different one-dimensional seed chaotic maps under the sine transform framework for subsequent block rotation, DNA dynamic encoding and decoding, generating key images for DNA XOR calculation. Simulation experiments and comparative analysis have shown that this algorithm can achieve fantastic encryption performance, resist various attacks, and have higher security levels and good generalization performance.

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

An Efficient Multi-Class Privacy-Preserving-Based Encryption Framework for Large Distributed Databases

This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches.

Hyperchaotic Maps and the Single Neuron Model: A Novel Framework for Chaos-Based Image Encryption

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. The main advantages of this work are (1) vast key space and (2) high encryption efficiency. The superior key space of 22551 is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of using the SNM as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite. Average achieved values for the metrics include MSE of 9626, PSNR of 8.3 dB, MAE of 80.99, entropy of 7.999, NPCR of 99.6% and UACI of 31.49%.