Encryption Framework Research Articles

AI-Powered Encryption Revolutionizing Cybersecurity with Adaptive Cryptographic Algorithms

This paper proposes a state-of-the-art encryption technique that integrates artificial intelligence into a dynamic, content-aware security system. We introduce an AI-powered encryption framework that automatically adjusts cryptographic parameters based on message sensitivity, effectively balancing security requirements and computational efficiency. The system combines a DistilBERT-based neural network for real-time content sensitivity analysis with a flexible encryption mechanism that adapts key lengths, iteration counts, and entropy levels on the fly. Our implementation demonstrates significant adaptability, with a correlation of 0.974 between content sensitivity and security parameters. The system distinguishes between different security requirements, using 32-byte keys with millions of iteration rounds for high-sensitivity content (sensitivity score of 8.64) and 16-byte keys with reduced iterations for low-sensitivity messages (sensitivity score of 3.10). The processing time scales linearly with security requirements, ranging from 300 ms for low-sensitivity content to 732 ms for high-security encryption. Performance evaluation was highly effective, with the system achieving an overall score of 8.64/10, including 9.87/10 for adaptability and 9.12/10 for performance efficiency. The security level rated high at 7.37/10 while maintaining manageable computational overhead. The framework effectively handled different types of content without sacrificing encryption-decryption accuracy across all levels of sensitivity. This work signifies a significant leap forward in the field of adaptive cryptography, demonstrating the capabilities of AI-driven security systems that can automate and optimize encryption parameters without compromising security standards. These results suggest that this approach may well represent the future of encrypted communications, providing scaled security appropriately without human intervention.

An intelligent and efficient CNN-AES framework for image block encryption with a multi-key approach

Abstract The integration of cryptography and deep learning has become known as a promising way to improving image security in the context of escalating cyber threats, particularly in areas requiring secure image transmission. The proposed methodology involves a Convolutional Neural Networks model designed to encode 256 × 256 images, followed by partitioning the encoded output into 16 blocks and encrypting each block using the AES algorithm with 16 unique keys derived from an initial single key to secure image data. Extensive evaluation of the framework’s effectiveness is conducted using correlation analysis, which achieves a low correlation coefficient of approximately 0.03; high NPCR and UACI values of up to 99.4% and 51%, respectively; histogram analysis; PSNR; MSE; MAE; and the NIST test suite, among other metrics. The outcomes show that the framework is highly resistant to differential assaults and maintains minimal loss of image quality during the encryption and decryption processes. The approach addresses important issues in digital information security and unlocks the way to safer digital communications. It has major practical implications for private content sharing on social media platforms, secure medical imaging transmission, and the management of sensitive surveillance data. A comprehensive analysis shows that the proposed encryption algorithm works more effectively than the techniques presently in use for image encryption. This work highlights how deep learning and cryptography techniques can be combined to enhance image security as well as offer a robust solution to protect sensitive image data against cyber threats.

Two-Fold Cryptography: Enhancing Image Security with Henon Map

This paper introduces an innovative method for image encryption called "Two-Fold Cryptography," which leverages the Henon map in a dual-layer encryption framework. By applying two distinct encryption processes, this approach offers enhanced security for images. Key parameters generated by the Henon map dynamically shape both stages of encryption, creating a sophisticated and robust security system. The findings reveal that Two-Fold Cryptography provides a notable improvement in image protection, outperforming traditional single-layer encryption techniques.

A hybrid encryption framework leveraging quantum and classical cryptography for secure transmission of medical images in IoT-based telemedicine networks

In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods. The proposed framework not only secure medical data against cyber threats but also protects the secret security keys. Initially, a Quantum Key Distribution (QKD) is employed to generate a shared key, which is then used to secure the symmetric keys via One-Time Pad (OTP) encryption. Next, bit-planes are extracted from each color component. The rows and columns of the extracted bit-planes are scrambled using random sequences which are generated by a 6D hyperchaotic Chen system and the Ikeda map. To further increase confusion in the original data, multiple-step pixel scrambling operations such as pixel shuffling, pixel value shuffling, and rotational and flipping operations are implemented. After the confusion phase, a combination of affine transformations with non-linear functions, Discrete Cosine Transform (DCT) with complex modulation, Discrete Wavelet Transform (DWT) with random phase modulation, bilinear transformation, and nonlinear polynomial mapping are employed to create diffusion in the scrambled components. These multiple encryption operations aim to maximize randomness in the final ciphertext image. Additionally, to reduce computational complexity, only the Most Significant Bit-Planes (MSBs) are encrypted, as they contain more than 94% of the plaintext information. Several experimental results and analyses are conducted to assess the proposed encryption framework, including entropy analysis, key sensitivity analysis, correlation analysis lossless analysis, and histogram analysis. Furthermore, the framework is tested against various cyberattacks such as brute-force attacks, clipping attacks, and noise attacks on the ciphertext images, to demonstrate its resilience against such threats.

A novel asymmetric encryption framework based on a 2D hyperchaotic map and enhanced S-box for secure medical image transmission

Abstract Recently, the advent of Internet of Medical Things (IoMT) has effectively alleviated the problem of difficulty in accessing medical services. However, during telemedicine, various medical images containing sensitive private information are exposed in communication channels. Therefore, there is an immediate need for an effective encryption method to ensure the secure transmission of medical images. In this paper, an image encryption algorithm is proposed based on a new chaotic model and an enhanced S-box. Furthermore, the proposed encryption algorithm is applied within a novel asymmetric image encryption framework. Firstly, to address the problems of narrow chaotic intervals and uneven trajectory distribution present in some existing chaotic maps, a two-dimensional cross-sine-modular model (2D-CSMM) is constructed. Secondly, in conjunction with the DNA algorithm, an enhanced S-box is proposed. Finally, in order to effectively protect key transmission as well as to enhance the system's resistance against chosen plaintext attacks, a novel asymmetric image encryption framework is designed by integrating the Elliptic Curve Diffie-Hellman (ECDH), Elliptic Curve Cryptography (ECC) and SHA-256 functions. And the secret key is computed from the cipher key, the shared key, and the native key associated with the plain image. Comprehensive experimental results demonstrate the high efficiency and the resilience of this new algorithm against common attacks.

Differential sensing approaches for scattering-based holographic encryption

Abstract We develop a new scattering-based framework for the holographic encryption of analog and digital signals. The proposed methodology, termed ‘differential sensing’, involves encryption of a wavefield image by means of two hard-to-guess, complex and random scattering media, namely, a background and a total (background plus scatterer) medium. Unlike prior developments in this area, not one but two scattering media are adopted for scrambling of the probing wavefields (as encoded, e.g. in a suitable ciphertext hologram) and, consequently, this method offers enhanced security. In addition, while prior works have addressed methods based on physical imaging in the encryption phase followed by computational imaging in the decryption stage, we examine the complementary modality wherein encryption is done computationally while decryption is done analogically, i.e. via the materialization of the required physical imaging system comprising the ciphertext hologram and the two unique (background and total) media. The practical feasibility of the proposed differential sensing approach is examined with the help of computer simulations incorporating multiple scattering. The advantages of this method relative to the conventional single-medium approach are discussed for both analog and digital signals. The paper also develops algorithms for the required in situ holography as well as a new wavefield-nulling-based approach for scattering-based encryption with envisioned applications in real-time customer validation and secure communication.

Chaos-based Cryptography for Voice Communication Security

Chaos-based cryptography represents a groundbreaking approach to securing voice communications, leveraging chaotic systems to integrate unpredictability and randomness into encryption keys. This innovative methodology protects against eavesdropping, man-in-the-middle (MitM) attacks, and spyware, safeguarding sensitive conversations. By utilizing chaos theory to encrypt voice signals, Chaos- based cryptography strictly limits access to authorized parties, maintaining confidentiality and data integrity. Notably, this chaos-based encryption framework delivers exceptional voice transmission speed and reliability, making it ideal for critical applications demanding high communication velocity and precision. Chaos- based cryptography also adheres to regulatory standards for secure communication, guaranteeing compliance across various industries. Consequently, this cutting-edge technology is highly sought after for enhancing voice communication system security. The study presents an overview of the chaos- based cryptography for voice communication security.

Enhancing Internet of Things communications: Development of a new S-box and multi-layer encryption framework

Enhancing Internet of Things communications: Development of a new S-box and multi-layer encryption framework

Improved Lightweight Image Encryption and Decryption for Medical IoT Devices Using 6D Chaotic Maps with XOR Diffusion, Permutation and Substitution

This research introduces a lightweight image encryption framework specifically designed for medical IoT devices, utilising a 6D chaotic map in conjunction with XOR diffusion, pixel permutation, and an optional substitution layer. The methodology utilises the intrinsic randomness, ergodicity, and sensitivity of high-dimensional chaotic systems to achieve robust encryption and secure transmission of sensitive medical images, including X-rays, MRIs, and ECGs. Comprehensive evaluations indicate that the framework effectively disrupts spatial coherence, attaining nearly zero pixel correlation and high entropy (~8), while maintaining computational efficiency suitable for resource-constrained IoT environments. The encryption scheme demonstrates significant sensitivity to input variations, with an average NPCR of 99.6% and a UACI surpassing 33%, highlighting its robustness against differential and statistical attacks. The comparative analysis of traditional and lower-dimensional chaotic encryption methods reveals that the proposed algorithm offers a superior balance between cryptographic security and performance. The findings demonstrate that the proposed system is a feasible solution for real-time, secure image processing in medical IoT applications. Future research will investigate adaptive parameter tuning and the integration of machine learning to improve encryption efficiency and robustness.

A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.

HMC-FHE: A Heterogeneous Near Data Processing Framework for Homomorphic Encryption

HMC-FHE: A Heterogeneous Near Data Processing Framework for Homomorphic Encryption

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

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.

Multi-image encryption based on 3D space scrambling and new spatiotemporal chaotic system

This paper introduces a groundbreaking spatiotemporal chaotic system, named DCMLMDF, and a novel encryption method that synergizes scrambling and diffusion synchronization for multi-image encryption. The DCMLMDF system, which incorporates a dynamic coupling approach and a random delay feedback mechanism, significantly enhances the randomness and complexity of the encryption process. By applying this system within the newly designed multi-image encryption framework, the method achieves three-dimensional space scrambling and diffusion synchronization, overcoming traditional encryption challenges such as extended encryption time and periodic vulnerabilities. The results demonstrate that this innovative approach not only effectively confuses image data but also substantially improves overall system security, marking a significant advancement in the application of chaotic systems to image encryption.

An MLWE-Based Cut-and-Choose Oblivious Transfer Protocol.

The existing lattice-based cut-and-choose oblivious transfer protocol is constructed based on the learning-with-errors (LWE) problem, which generally has the problem of inefficiency. An efficient cut-and-choose oblivious transfer protocol is proposed based on the difficult module-learning-with-errors (MLWE) problem. Compression and decompression techniques are introduced in the LWE-based dual-mode encryption system to improve it to an MLWE-based dual-mode encryption framework, which is applied to the protocol as an intermediate scheme. Subsequently, the security and efficiency of the protocol are analysed, and the security of the protocol can be reduced to the shortest independent vector problem (SIVP) on the lattice, which is resistant to quantum attacks. Since the whole protocol relies on the polynomial ring of elements to perform operations, the efficiency of polynomial modulo multiplication can be improved by using fast Fourier transform (FFT). Finally, this paper compares the protocol with an LWE-based protocol in terms of computational and communication complexities. The analysis results show that the protocol reduces the computation and communication overheads by at least a factor of n while maintaining the optimal number of communication rounds under malicious adversary attacks.

Securing blockchain-enabled smart health care image encryption framework using Tinkerbell Map

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.

Transparent Encryption for External Storage Media with Mobile-Compatible Key Management by Crypto Ciphershield

Evidently, there is a widespread adoption of external storage devices in contemporary situations, including USB sticks, SD cards, and flash memory devices. If, on the other hand, these devices are misplaced or lost, the presence of sensitive data on them can provide a potential security risk. The currently available encryption solutions frequently have usability issues, which necessitate that users continually input keys or login credentials across a variety of portable devices. The study offers a solution that overcomes these difficulties by integrating key caching with time-delayed deletion within the MobileShielded Encryption Framework (MoSEF). This method is our reaction to the problem. There are two distinct iterations of this idea that we have developed. The initial version, which functions flawlessly even in the absence of deliberate user intervention, achieves this by restricting the use of external storage to only temporary data transfers.Within the file system, the second variant makes it possible to handle numerous encryption keys for different files. This is made possible by a trusted host on the file system. By eliminating the need to share keys or passwords, timed key caching significantly improves both the level of security and the level of usability of a system. In addition to ensuring interoperability with mobile devices, our solution, which is incorporated within the MoSEF architecture, provides plaintext encryption for external storage media. To reduce the likelihood of data breaches and illegal access, this strategy offers users a mechanism that is both convenient and safe for managing sensitive data when they are on the move.

Chaos based image encryption scheme to secure sensitive multimedia content in cloud storage

Cloud computing offers a variety of on-demand services to users and has gained significant prominence in the contemporary era. The security of information stored in cloud data centers has become a central concern, especially for sensitive data like medical images, videos, and multimedia content that require heightened protection when stored in data centers. Cloud users have a responsibility to ensure the security of their data through strategic measures. Focusing on maintaining the privacy of images stored in the cloud, this research introduces an innovative image encryption technique that is based on a modified Skew Tent chaotic map. The suggested modification to the chaotic map includes combining the Skew Tent map with both the sine trigonometric function and perturbation technology. This results in enhanced randomness, more intricate dynamical behavior, a broader chaotic range, and increased sensitivity to initial values in contrast to alternative chaotic maps. The incorporation of this adapted map into a stepwise procedure, involving two rounds of permutation followed by diffusion, effectively accomplishes image data encryption for cloud storage systems. These consecutive operations collectively enhance the encryption method’s randomness and robustness. Through simulations conducted using Cloudsim, the cipher-image exhibits a uniform distribution and achieves a commendable information entropy score of 7.996749. The encryption algorithm significantly reduces correlation coefficients from 1 in the original image to 0 in the encrypted image, while maintaining NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) values within the critical range. Additionally, both theoretical analysis and practical evaluations confirm the algorithm’s resilience against exhaustive, occlusion, and classical attacks. Moreover, extending this encryption framework to video data, a novel video encryption approach is proposed.

An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security

This study proposes an optimized hybrid encryption framework combining ECC-256r1 with AES-128 in EAX mode, tailored for smart home healthcare environments, and conducts a comprehensive investigation to validate its performance. Our framework addresses current limitations in securing sensitive health data and demonstrates resilience against emerging quantum computing threats. Through rigorous experimental evaluation, we show that the proposed configuration outperforms existing solutions by delivering unmatched security, processing speed, and energy efficiency. It employs a robust yet streamlined approach, meticulously designed to ensure simplicity and practicality, facilitating seamless integration into existing systems without imposing undue complexity. Our investigation affirms the framework's capability to resist common cybersecurity threats like MITM, replay, and Sybil attacks while proactively considering quantum resilience. The proposed method excels in processing speed (0.006 seconds for client and server) and energy efficiency (3.65W client, 95.4W server), offering a quantum-resistant security level comparable to AES-128. This represents a security-efficiency ratio of 21.33 bits per millisecond, a 25.6% improvement in client-side processing speed, and up to 44% reduction in server-side energy consumption compared to conventional RSA-2048 methods. These improvements enable real-time encryption of continuous health data streams in IoT environments, making it ideal for IoT devices where AES-128′s smaller footprint is advantageous. By prioritizing high-grade encryption alongside ease of use and implementation, the proposed framework presents a future-proof solution that anticipates the trajectory of cryptographic standards amid advancing quantum computing technologies, signifying a pivotal advancement in safeguarding IoT-driven healthcare data.