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The Rabin Cryptosystem is a public-key cryptosystem known for providing security levels comparable to RSA but with reduced computational overhead. Despite these advantages, it has not been widely adopted for practical use due to its lack of a deterministic nature. This paper addresses this limitation by introducing a new Deterministic Rabin Cryptosystem (DRCS). The DRCS framework includes processes for key generation, encryption, and decryption, leveraging the concept of cubic congruence and the Chinese Remainder Theorem to ensure the decryption process is unambiguous and deterministic. This design not only retains the computational efficiency of the original Rabin Cryptosystem but also enhances its security. Our comparative analysis shows that the DRCS achieves similar performance to the traditional Rabin system in terms of computational overhead. While the encryption process in DRCS is less demanding, its decryption process is more complex, and overall, it maintains a polynomial time complexity. Furthermore, a detailed security analysis indicates that the DRCS is significantly harder to factorize compared to previous models, underscoring its improved security features.

Data exchange has been rapidly increased recently by increasing the use of mobile networks. Sharing information (text, image, audio and video) over unsecured mobile network channels is liable for attacking and stealing. Encryption techniques are the most suitable methods to protect information from hackers. Hill cipher algorithm is one of symmetric techniques, it has a simple structure and fast computations, but weak security because sender and receiver need to use and share the same private key within a non-secure channel. Therefore, a novel hybrid encryption approach between elliptic curve cryptosystem and hill cipher (ECCHC) is proposed in this paper to convert Hill Cipher from symmetric technique (private key) to asymmetric one (public key) and increase its security and efficiency and resist the hackers. Thus, no need to share the secret key between sender and receiver and both can generate it from the private and public keys. Therefore, the proposed approach presents a new contribution by its ability to encrypt every character in the 128 ASCII table by using its ASCII value direct without needing to assign a numerical value for each character. The main advantages of the proposed method are represented in the computation simplicity, security efficiency and faster computation.

This paper is the first of two complementary papers. In this paper, we present a novel algorithm in the field of cryptography, an area that has garnered significant attention from researchers. Our algorithm is based on matrix calculus over finite rings, enabling efficient encryption and decryption processes without requiring additional computational resources. A key feature of this approach is its formulation of a mathematical problem involving the solution of a system of nonlinear equations, which significantly enhances the system’s security and complexity. Furthermore, we provide a detailed mathematical proof of the algorithm’s correctness.

To achieve confidentiality, authentication, integrity of medical data, and support fine-grained access control, we propose a secure electronic health record (EHR) system based on attribute-based cryptosystem and blockchain technology. In our system, we use attribute-based encryption (ABE) and identity-based encryption (IBE) to encrypt medical data, and use identity-based signature (IBS) to implement digital signatures. To achieve different functions of ABE, IBE and IBS in one cryptosystem, we introduce a new cryptographic primitive, called combined attribute-based/identity-based encryption and signature (C-AB/IB-ES). This greatly facilitates the management of the system, and does not need to introduce different cryptographic systems for different security requirements. In addition, we use blockchain techniques to ensure the integrity and traceability of medical data. Finally, we give a demonstrating application for medical insurance scene.

In this work, we propose a novel 3D chaotic map obtained by coupling the piecewise and logistic maps. Showing excellent properties, like a high randomness, a high complexity and a very long period, this map has enabled us to implement and investigate a new chaotic pseudo-random number generator (CPRNG). The produced pseudo-random numbers exhibit a uniform distribution and successfully pass the NIST SP 800-22 randomness tests suite. In addition, an application in the field of color image encryption is proposed where the encryption key is strongly correlated with the plain image and is then used to perform the confusion and diffusion stages. Furthermore, the ability to expand the size of our map has an impact on the complexity of the system and increases the size of the key space, making our cryptosystems more efficient and safer. We also give some statistical tests and computer simulations which confirm that the proposed algorithm has a high level of security.

Recent advancements in multimedia systems have increased the demand for strong digital image security mechanisms. Traditional cryptosystem evaluations, dependent on manual statistical analysis, are computationally exhaustive and not scalable. This study proposes a machine learning-based framework, such as a Support Vector Machine (SVM) classifier, for the categorization of image encryption levels into three discrete classes: Strong, Acceptable, and Weak. The model is trained using statistical descriptors such as Peak Signal to Noise Ratio (PSNR), entropy, Mean Square Error (MSE), energy, correlation, homogeneity, and contrast extracted from encrypted image datasets. Feature normalization techniques, StandardScaler, have been used to ensure balanced input contributions. The proposed system, i.e., SVM with a Radial Basis Function (RBF) kernel, outperforms other kernels. The performance of the proposed model shows an average classification accuracy of 98%, precision up to 100%, and an F1-score of 97%. A web-based interface developed using Django integrates the model, enabling real-time analysis and visualization, making the proposed system a scalable solution for cryptographic strength evaluation in image security applications.

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle–Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

In this work, we consider a class of mappings over bit vectors which imitate the multiplication by small constants with pure logic operations and non-cyclic shifts. Such mappings can provide non-linearity and strengthen the design of LRXcryptosystems, which are widely used in lightweight cryptography, due to their apparent benefits: a simple implementation and the absence of internal rotational symmetry, which increases security against rotational attacks. We examine the security of these mappings against differential cryptanalysis. We provide an explicit easy-to-calculate expression of differential probabilities for several versions of LRX-analogues of small constant multiplication with different operations and shift values.

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.