Search Results (17)

Electronic voting systems inherently encompass a structural tension among ballot secrecy, verifiability, and coercion resistance. Voters must be able to verify whether their votes have been included; however, if such verification information can serve as evidence presentable to a third party, it becomes a basis for post-election intimidation. Existing studies have focused primarily on performance evaluation or data separation, and have not comprehensively addressed the structural tension between verifiability and coercion resistance. This study defines this tension as the verification paradox and designs and implements an electronic voting prototype on a three-organization consortium based on Hyperledger Fabric 2.5, combining a 2-of-3 endorsement policy, nullifier-based anonymity, Exponential ElGamal homomorphic tallying, zero-knowledge proof (ZKP)-based ballot validity verification, panic-password-based deniable verification, and Private Data Collection (PDC)-based coerced vote separation. Quantitative evaluation results confirm a server latency overhead of +0.9% for ElGamal relative to the AES performance baseline, statistical indistinguishability between normal and panic responses ($p>0.05$), and a peak throughput of approximately 40.7 TPS (with an error rate of 0%) under 1000 concurrent voters. Through this prototype implementation and quantitative evaluation, we show the potential of permissioned blockchains to partially and practically mitigate the verification paradox. This study, however, does not provide a formal security proof, and it is subject to a trust assumption on PDC as well as to the experimental limitations of a single evaluation environment and a limited load range. Full article

We propose a secure biometric data protection (SBDP) system, which uses artificial intelligence (AI) and encryption methods to prevent forgery and keep the biometric data private and intact. The proposed SBDP approach integrates deep learning-based feature extraction with robust encryption and authentication mechanisms in a single pipeline. We use the optimized convolutional neural network (OCNN) to obtain unique features from multimodal biometric inputs like fingerprints, facial photos, and retinal scans. This works well because it learns how to represent data efficiently. To reduce the risks of raw biometric exposure, we adopt a generative adversarial network (GAN) to generate synthetic biometric representations that maintain essential characteristics while reducing sensitivity to data leakage. The biometric features and images are encrypted using the ElGamal cryptosystem to provide security assurance, while the digital signature scheme based on the SHA-256 hash function is used to provide data integrity and authenticity. Experimental results show good performance of all components of the framework. The optimized CNN obtains a classification accuracy of more than 99.8%, while the GAN shows stable training behavior with the discriminator and generator losses converging to around 0.3 and 4.0, respectively. The cryptographic module guarantees encryption dependability and signature verification efficacy across all evaluated scenarios. The integrated system provides effective protection of biometric data from unauthorized access, tampering and identity forgery. The SBDP framework is a promising solution for defense, healthcare and digital identity management, ensuring secure transmission and storage of biometric data. Full article

Encryption protects data in the cloud but adds energy cost, especially for partially homomorphic encryption (PHE) schemes that allow computation on encrypted data. Their carbon footprint across cloud data center deployments remains underexplored. We benchmark eight PHE algorithms from the LightPHE open-source Python library, including RSA, ElGamal, Exponential ElGamal, Paillier, Damgård–Jurik, Okamoto–Uchiyama, Goldwasser–Micali, and Elliptic Curve ElGamal, across six cloud environments, and use timing data as input to a carbon estimation model covering Scope 1, Scope 2, and Scope 3 emissions across ten data center configurations. We ground the energy model with a dedicated Intel RAPL calibration on bare-metal hardware using 30 repetitions per configuration. The calibration measures average CPU package power at 34.7 W and total system power at 48.4 W, showing that a fixed 150 W CPU-only assumption overestimates actual CPU power by a factor of 4.3. We present calibrated estimates alongside a 150 W server-class scenario and a sensitivity analysis across power, PUE, and grid carbon intensity. Elliptic curve schemes provide equivalent classical security at a fraction of the energy cost of RSA, and algorithm-specific mathematical structure drives order-of-magnitude differences in carbon output. These results reveal an asymmetry between security and carbon cost across PHE algorithms and establish a sustainable-cryptography baseline for future PQC-based homomorphic schemes. Full article

The integration of artificial intelligence (AI) and blockchain technology opens new avenues for decentralized, transparent, and secure data-driven systems. However, ensuring privacy and verifiability in collaborative AI environments remains a key challenge, especially when model updates or decisions must be recorded immutably on-chain. In this paper, we propose a novel privacy-preserving framework that leverages an ElGamal-based aggregate signature scheme with aggregate public keys to enable secure, verifiable, and unlinkable multi-party contributions in blockchain-based AI ecosystems. This approach allows multiple AI agents or data providers to jointly sign model updates or decisions, producing a single compact signature that can be publicly verified without revealing the identities or individual public keys of contributors. The design is particularly well-suited to resource-constrained or privacy-sensitive applications such as federated learning in healthcare or finance. We analyze the security of the scheme under standard assumptions and evaluate its efficiency in different terms. The study and experimental results demonstrate the potential of our framework to enhance trust and privacy in AI collaborations over decentralized networks. Full article

Modular multiplication is a pivotal operation in public-key cryptosystems such as RSA, ElGamal, and ECC. Modular multiplication design is crucial for improving overall system performance due to the large-bit-width operation with high computational complexity. This paper provides a classification of integer multiplication algorithms based on their implementation principles. Furthermore, the core concepts, implementation challenges, and research advancements of multiplication algorithms are systematically summarized. This paper also gives a brief overview of modular reduction algorithms for various types of moduli and discusses the implementation principles, application scenarios, and current research results. Finally, the detailed research development of modular multiplication algorithms in four major classes over prime fields is deeply analyzed and summarized, making it essential as a guide for future research. Full article

The rapid expansion of distributed systems such as the Internet of Things (IoT) has increased the need for robust authentication and data integrity mechanisms to ensure public security in dynamic environments. This article presents a hierarchical multiparty digital signature (HMPS) technique designed to address the unique challenges of resource-constrained and decentralized systems. By integrating a modified ElGamal-based individual signature with linear encryption and hierarchical aggregation, HMPS delivers enhanced security through collaborative and layered signing processes. A key application is demonstrated in intelligent vehicle surveillance, where the scheme ensures the authenticity and integrity of commands and data in multi-level communication scenarios. Comprehensive security analysis confirms resistance to forgery, single points of failure, and unauthorized access. HMPS exhibits superior computational efficiency, scalability, and energy efficiency, as evidenced by comparative performance evaluations with state-of-the-art techniques. These results highlight HMPS as a highly effective solution for secure, real-time IoT applications, providing a pathway to more resilient and trustworthy distributed systems. Full article

During the last five years, spiking neural P (SN P) systems have attracted a lot of attention in the field of cryptography since these systems can more efficiently support advanced and complex cryptographic algorithms due to their high computational capabilities. Specifically, these systems can be seen as a potential solution to efficiently performing asymmetric algorithms, which are more demanding than symmetric systems. This factor becomes critical, especially in resource-constrained single-board computer systems, since many of these systems are currently used to ensure the security of IoT applications in portable systems. In this work, we present for the first time the implementation of an asymmetric encryption algorithm called ElGamal based on spiking neural P systems and their cutting-edge variants. The proposed design involves the encryption and decryption processes. Specifically, we propose the design of a neural network to efficiently perform the extended Euclidean algorithm used in the decryption task. Here, we exert major efforts to create a compact and high-performance circuit to perform the extended Euclidean algorithm since the calculation of this algorithm is the most demanding when the decryption process is required. Finally, we perform several tests to show the computational capabilities of our proposal in comparison to conventional implementations on single-board computer systems. Our results show that the proposed encryption/decryption scheme potentially allows its use to ensure confidentiality, data integrity, and secure authentication, among other applications for resource-constrained embedded systems. Full article

Quantum illumination (QI) is an entanglement-based protocol for improving LiDAR/radar detection of unresolved targets beyond what a classical LiDAR/radar of the same average transmitted energy can do. Originally proposed by Seth Lloyd as a discrete-variable quantum LiDAR, it was soon shown that his proposal offered no quantum advantage over its best classical competitor. Continuous-variable, specifically Gaussian-state, QI has been shown to offer a true quantum advantage, both in theory and in table-top experiments. Moreover, despite its considerable drawbacks, the microwave version of Gaussian-state QI continues to attract research attention. A recent QI study by Armanpreet Pannu, Amr Helmy, and Hesham El Gamal (PHE), however, has: (i) combined the entangled state from Lloyd’s QI with the channel models from Gaussian-state QI; (ii) proposed a new positive operator-valued measurement for that composite setup; and (iii) claimed that, unlike Gaussian-state QI, PHE QI achieves the Nair–Gu lower bound on QI target-detection error probability at all noise brightnesses. PHE’s analysis was asymptotic, i.e., it presumed infinite-dimensional entanglement. The current paper works out the finite-dimensional performance of PHE QI. It shows that there is a threshold value for the entangled-state dimensionality below which there is no quantum advantage, and above which the Nair–Gu bound is approached asymptotically. Moreover, with both systems operating with error-probability exponents 1 dB lower than the Nair–Gu bound, PHE QI requires enormously higher entangled-state dimensionality than does Gaussian-state QI to achieve useful error probabilities in both high-brightness (100 photons/mode) and moderate-brightness (1 photon/mode) noise. Furthermore, neither system has an appreciable quantum advantage in low-brightness (much less than 1 photon/mode) noise. Full article

This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method. Full article

Modern technologies like quantum and cloud computing have reshaped computing, offering immense power and scalability. While beneficial, they also challenge the security of traditional cryptographic systems. Quantum computing threatens RSA and ECC with algorithms like Shor’s algorithm, which can accelerate computations exponentially. This risks exposing these systems to attacks, necessitating quantum-resistant cryptography. Cloud computing poses data security concerns, requiring robust cryptographic mechanisms and access controls. Lattice-based cryptography, leveraging problems like the Short Integer Solution (SIS), emerges as a solution. This paper presents a novel quantum-resistant public key encryption scheme based on ElGamal and SIS, ensuring security against quantum and classical threats in modern cryptographic environments. Full article

Today, safeguarding sensitive content through encryption is crucial. This work presents a hybrid cryptosystem for images that employs both asymmetric and symmetric encryption. The asymmetric component involves applying the Diffie–Hellman protocol and the ElGamal cryptosystem to securely transmit two constants. These constants are necessary for the symmetrical aspect to generate dynamic permutations, substitution boxes, and round keys. Following an encryption process with fourteen rounds, the encrypted images are processed by an algorithm proposed to enhance entropy, a critical metric for assessing encryption quality. It increases the frequencies of the basic colors to achieve a histogram closely resembling a uniform distribution, but it increases the image size by approximately 8%. This improves the entropy values achieved by the hybrid cryptosystem, bringing them remarkably close to the ideal value of 8.0. In specific instances, the entropy values were elevated from 7.99926 to 8.0. The proposed method exhibits resilience against various attacks, including differential, linear, brute force, and algebraic attacks, as evaluated through the entropy, correlation, goodness of fit, Discrete Fourier Transform (DFT), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Avalanche Criteria (AC), contrast, energy, and homogeneity. Further, encrypted images are subjected to noise attacks ranging from 20% to 50% noise, including additive, multiplicative, occlusion noise, as well as the newly introduced ${\chi }^2$ noise. The noise damage is quantified using the proposed Similarity Parameter (SP), and a 3 × 3 median filter is employed to enhance the visual quality. Full article

Images with sensitive content require encryption for storage and transmission. Symmetric schemes can cipher them, while an asymmetric cryptosystem can distribute the secret key safely. For this reason, we propose a dynamic hybrid cryptosystem, which ciphers images and transfers its private keys. It has a symmetric algorithm that applies the Lorenz equations for generating different boxes and permutations in every encryption process and round. Since the secret key concatenates two private numbers, an asymmetric algorithm is included for its key distribution. The proposal uses the Diffie–Hellman protocol with ElGamal for obtaining a seed and building 128 strings. Then, the SHA-512 is applied in each of them a number of times associated with the secret key value in its blockchain representation. The resultant strings are concatenated to conform to the public key. Finally, the tests indicate that the cryptosystem resists differential, linear, algebraic, and brute-force attacks. Its cipher quality is high according to the entropy, correlation, DFT, NPCR, UACI, AC, texture analysis, and goodness of fit test. Additionally, occlusion, additive, multiplicative, and the proposed ${\chi }^2$ noise attacks are simulated on encrypted images. Finally, the sharpness loss is measured with the Similarity Parameter and improved with a filter 5 × 5. Full article

The Internet of Things (IoT) is a kind of advanced information technology that has grabbed the attention of society. Stimulators and sensors were generally known as smart devices in this ecosystem. In parallel, IoT security provides new challenges. Internet connection and the possibility of communication with smart gadgets cause gadgets to indulge in human life. Thus, safety is essential in devising IoT. IoT contains three notable features: intelligent processing, overall perception, and reliable transmission. Due to the IoT span, the security of transmitting data becomes a crucial factor for system security. This study designs a slime mold optimization with ElGamal Encryption with a Hybrid Deep-Learning-Based Classification (SMOEGE-HDL) model in an IoT environment. The proposed SMOEGE-HDL model mainly encompasses two major processes, namely data encryption and data classification. At the initial stage, the SMOEGE technique is applied to encrypt the data in an IoT environment. For optimal key generation in the EGE technique, the SMO algorithm has been utilized. Next, in the later stage, the HDL model is utilized to carry out the classification process. In order to boost the classification performance of the HDL model, the Nadam optimizer is utilized in this study. The experimental validation of the SMOEGE-HDL approach is performed, and the outcomes are inspected under distinct aspects. The proposed approach offers the following scores: 98.50% for specificity, 98.75% for precision, 98.30% for recall, 98.50% for accuracy, and 98.25% for F1-score. This comparative study demonstrated the enhanced performance of the SMOEGE-HDL technique compared to existing techniques. Full article

To address a business need, most organizations allow employees to use their own devices to enhance productivity and job satisfaction. For this purpose, the Bring Your Own Device (BYOD) policy provides controllable access for employees to organize data through their personal devices. Although the BYOD practice implies plenty of advantages, this also opens the door to a variety of security risks. This study investigates these security risks and proposes a complementary encryption approach with a digital signature that uses symmetric and asymmetric algorithms, depending on the organization’s digital certificate, to secure sensitive information stored in employees’ devices within the framework of BYOD policies. The method uses Advanced Encryption System (AES), Blowfish, RSA and ElGamal with a digital signature to achieve strong encryption and address critical security considerations such as user authentication, confidentiality and data integrity. The proposed encryption approach offers a robust and effective cryptographic solution for securing sensitive information in organizational settings that involve BYOD policies. The study includes experimental results demonstrating the proposed approach’s efficiency and performance, with reasonable encryption and decryption times for different key and file sizes. The results of the study revealed that AES and Blowfish have the best execution time. AES has a good balance of security and performance. RSA performs better than ElGamal in encryption and signature verification, while RSA is slower than ElGamal in decryption. The study also provides a comparative analysis with previous studies of the four encryption algorithms, highlighting the strengths and weaknesses of each approach. Full article

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures. Full article