Key Size Research Articles

Minimising the Cipher Length of Elliptic Curve Cryptography Basing on Chebyshev Polynomial

Introduction: Public Key Cryptography (PKC) is one of the key components of contemporary cryptographic systems, mainly employed for securing digital communications using protocols such as SSL and TLS. PKC relies on trapdoor functions, which are computationally easy in one direction but impossible to invert without access to a secret key. RSA and the Diffie-Hellman key exchange protocol are classical algorithms that have been extensively employed for secure data transfer. More recently, Elliptic Curve Cryptography (ECC) has been in the limelight because of its high security with compact key sizes and fast computation, based on the algebraic structure of elliptic curves. Objectives: This study will seek to alleviate one of the most significant weaknesses of ECC-based encryption—namely the expansion of ciphertext size in ElGamal encryption. The goal is to create a revised ECC-based encryption scheme that is highly secure while reducing ciphertext size. Furthermore, the proposed approach will seek to include digital signature functionality for improved data authentication and integrity. Methods: The new encryption scheme is based on the ElGamal encryption scheme but with an addition involving Chebyshev polynomials of the first kind as a random value generator. This inclusion enables secure randomization during encryption. The new scheme guarantees that the ciphertext size is equal to the plaintext size, essentially compressing the output without loss of security. The scheme also includes digital signature functionality to ensure authenticity and non-repudiation. Results: The revised ECC-based encryption method proves to be an enormous advancement compared to conventional ElGamal encryption in terms of ciphertext length. Without compromising the strong security attributes of ECC, the new method attains ciphertext compression, maintaining the encrypted message size equal to the plaintext size. Additionally, the scheme efficiently accommodates digital signature operations, providing improved message integrity and verification. Conclusions: The improved encryption scheme overcomes a significant obstacle to ECC-based cryptography by decreasing ciphertext length at no cost in security. Combining Chebyshev polynomials and digital signature capability is a step towards better secure and efficient cryptographic protocols. The scheme has special application value for any scenarios involving light encryption with tight security assurances, e.g., secure messaging, cloud data encryption, and IoT transmission.

A Lightweight Encryption Method for IoT-Based Healthcare Applications: A Review and Future Prospects

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings.

Optimization of Lattice-Based Cryptographic Key Generation using Genetic Algorithms for Post-Quantum Security

The progress of quantum computing has posed serious threats to classical cryptographic systems, necessitating much research into developing post-quantum cryptography (PQC). Of the schemes available in PQC, the strongest candidates appear to be lattice-based cryptography (LBC), which encompasses an ample security basis and good computation efficiency. However, practically implementing LBC is faced with key-generation and optimization difficulties, mainly because of its enormous key sizes and computational overhead. The research proposes a novel concept whereby genetic algorithms (GAs) are blended with LBC to increase the merits of key generation while guaranteeing security. Through the evolutionary capacity of GAs, the proposed method optimizes lattice-based keys through selection, crossover, and mutation to ensure high entropy and computationally feasible with experimental results indicating that the GA-based method can cut down memory requirements and computational complexity, making it favorable for resource-constrained environments such as the Internet of Things and embedded systems. The method thus suggested accelerates encryption speed and simultaneously strengthens the security of the optimized key structures. This study emphasizes evolutionary algorithms’ potential to facilitate PQC advancement and provides a scalable and efficient framework for cryptographic systems.

Distribution pattern of meiofauna assemblages along an environment gradient on the coast of the Yellow and Bohai Seas.

Distribution pattern of meiofauna assemblages along an environment gradient on the coast of the Yellow and Bohai Seas.

An Image Encryption Algorithm by Using Secure Key Generation Techniques

To enhance the security and authentication processes, this extension integrates Elliptic Curve Cryptography (ECC) for image encryption and Local Binary Pattern Histogram (LBPH) for live face authentication during registration and login. ECC, known for its high security with smaller key sizes, is incorporated to securely encrypt images, addressing the growing need for efficient and lightweight image encryption in resource-constrained environments. By employing ECC, the encryption algorithm ensures that visual data transmitted and stored remains confidential, with reduced computational overhead compared to traditional encryption methods like AES. Additionally, LBPH is employed for real-time face authentication, allowing users to authenticate themselves via facial recognition during both registration and login. This biometric system leverages LBPH’s robustness against variations in lighting and facial expressions, ensuring reliable face recognition in diverse environments. By the integrity of user access control, offering a combining ECC for image encryption and LBPH for face authentication, the extended system strengthens both the confidentiality of transmitted data and seamless and secure authentication process. The enhanced system was evaluated for speed, accuracy, and efficiency, showing significant improvements in real-time security applications.

Block Chain Technology-Based Crypto-Go-Charity for Scholarship Donation Tracking Platform

In the philanthropy sector, new technology is required to address issues like lack of transparency, trust, and accountability, as well as extended processing times and expenses. So, the present research examines the application of blockchain (BC) technology to enhance the donation tracking Platform, which delivers data immutability, and transaction transparency, as well as peer-to-peer transactions, as well as it is all the rage in today's technological period. BC enables the tracking of every donation, by permitting the donors to track the usage of their funds. The three primary roles of the present research are nongovernmental organization, Donors, as well as scholar beneficiaries. Corporate social responsibility (CSR) funds of industries are considered as donations, which causes educational scholarship for the initial prototype building. Subsequently, this prototype is scalable to every feasible field of educational scholarship as well as other methods of philanthropy. Thus, a Crypto-Go-Charity is proposed to solve the problems of the supply chain by utilizing the Ethereum BC and delivering a more secure and immutable ecosystem for scholarships. Solidity language is utilized here to write smart contracts and InterPlanetary File System for document storage. The performance is demonstrated by calculating the evaluation metrics as the privacy ratio and computational time for 200 users with the number of verifications at 300, as well as a key size of 50 kb, is 97.52% and 89.28 s, respectively. Likewise, the detection rate and memory usage of the system acquired the value of 98% and 81.46 MB for 200 users with the number of verifications at 300 as well as a key size of 50 kb.

Enhancing Cloud Data Security using a Hybrid Cryptographic Model: A Combination of Advanced Encryption Standard and Elliptic Curve Cryptography

With cloud computing, users may easily access and store confidential data remotely over the internet at any time and from any location at a reasonable cost. But there is security risks associated with this convenience. Due to its third-party accessibility, data on the cloud is vulnerable to authentication and data integrity attacks. Furthermore, there is a higher chance of data exposure, leakage, and loss in different locations when several users access data simultaneously across different internet connections. Elliptic Curve Cryptography is one of the cryptographic techniques and protocols that have been developed to solve these concerns. To maintain security and integrity in cloud environments, this paper presents a hybrid cryptographic system to improve data security in cloud environments by combining the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC). The proposed model makes use of ECC to generate secure keys, which reduces key size while preserving strong security. This enhances the overall performance and efficiency of the encryption and decryption processes. By combining the advantages of ECC and AES, the framework ensures that data stored in the cloud remains secure, confidential, and only accessible to authorized users. In terms of data integrity and authentication, the study's findings demonstrate that the suggested hybrid approach performs better than traditional cryptographic methods. The experimental results show that, in comparison to existing methods, the proposed strategy is more efficient and produces better outcomes.

Unconditional Quantum Cryptography with a Bounded Number of Keys

We construct the following cryptographic primitives with unconditional security in a bounded-key model: * One-time public-key encryption, where the public keys are pure quantum states * One-time signatures, where the verification keys are pure quantum states. In our model, the adversary is given a bounded number of copies of the public key. We present efficient constructions and nearly-tight lower bounds for the size of the secret keys. Our security proofs are based on the quantum coupon collector problem, which was originally studied in the context of learning theory. The quantum coupon collector seeks to learn a set of strings (coupons) when given several copies of a superposition over the coupons. We make novel connections between this problem and cryptography. Our main technical ingredient is a family of coupon states, with randomized phases, that come with strong hardness properties. Our analysis improves on prior work by (i) showing that the number of quantum states needed to learn the entire set of coupons is identical to the number of random coupons needed in the classical coupon collector problem. (ii) Furthermore we prove that this result holds for a randomly chosen set of coupons, whereas prior work only lower-bounded the number of coupon states required to learn the worst-case set of coupons.

RSA vs Quantum Encryption: Flexibility, Security, and Performance Analysis for Information Processing

Introduction: With the advent of quantum computing, traditional encryption methods face significant challenges in maintaining security. This study explores quantum information processing through quantum communication, providing a comparative analysis of Rivest-Shamir-Adleman (RSA) encryption and quantum encryption. Objective: The study aims to analyze the flexibility of RSA and quantum encryption in handling varying data lengths and key sizes, compare their security levels against cryptographic threats, and evaluate their computational efficiency and overall performance. Methods: This study explores an innovative approach for handling quantum information via quantum communication. A detailed comparative analysis of RSA and quantum encryption was conducted based on key parameters such as data length, key size, and computational overhead. Results: When comparing RSA encryption to quantum encryption, RSA encryption consistently maintains a data length of around 37 units, whereas quantum encryption consistently maintains a data length of 100 units. Quantum encryption constantly use 24-bit keys, while RSA keys can have variable lengths ranging from 85 to 785 bits, depending on unique conditions. RSA consistently employs a private key size of 1547 bits, but quantum encryption can adjust the size of the private key based on the length of the password being used. The findings highlight the differences in flexibility and security between the two systems, with quantum encryption showing more flexibility and RSA providing consistent performance. Conclusion: The study highlights the need for quantum-safe cryptographic solutions as the threat of quantum computing grows. While RSA remains a reliable encryption method for current applications, quantum encryption provides a more robust and future-proof approach.

Fortifying Future IoT Security: A Comprehensive Review on Lightweight Post-Quantum Cryptography

This paper presents lightweight Post-Quantum Cryptography (PQC), identifying its importance for a shift from traditional cryptographic schemes, vulnerable to quantum threats, to efficient PQC algorithms. Lattice-based cryptography stands out owing to its small key sizes and computational efficiency, with CRYSTALS-Kyber and NTRU algorithms being substantial representatives for Internet of Things (IoT) applications. However, PQC implementation in IoT environments has various obstacles to overcome. Minimizing energy consumption, scalability, and hardware limitations remain key challenges for PQC smooth integration into these resource-constrained networks. The present review analyzes the state-of-the-art PQC, makes security and performance comparisons among leading algorithms, and evaluates optimization techniques aimed at reducing resource overheads. Algorithmic refinement, hardware acceleration, and hybrid cryptography are also discussed as methods for mitigating these challenges. The results indicate that continuous research and development efforts should be made to improve the PQC technologies, and thus achieve their practical deployment in IoT systems. Quantum threats in IoT will be, hence, prevented with the employment of secure and scalable IoT ecosystems in a post-quantum world.

Develop a quantum key distribution application based on the BB84 protocol combined with a classical channel

Amid the escalating concerns over internet security, quantum cryptography stands out as a highly promising solution for significantly enhancing the security of networking systems, emerging among them is the quantum key distribution (QKD) with the function of creating secret session keys a breeze when leveraging the intriguing properties of quantum mechanics. This study is rooted in the BB84 QKD method, where in the distribution process in the quantum realm is simulated to derive a shared key via a public channel connecting two clients with the assistance of a server, utilizing the quantum inspire (QI) platform to generate qubits within the BB84 protocol. The results, the findings regarding the performance of BB84 reveal that when the server is set up, and the key size increases to 4000 bits, the process of sending module takes 16.215 sec, the transfer module takes approximately 5.2 hours, the receive module takes 1.257 sec to finish the process for the final session key share. This indicates a noteworthy enhancement in the execution speed of QKD employing the BB84 protocol, which now holds the potential for reinforcing network security using quantum computing systems.

Implementation and Evaluation of Data Protection in Databases Using Symmetric Encryption Algorithms

In an era marked by rising cyber threats, ensuring database security and protecting sensitive information have become critical challenges. This study presents an experimental framework to evaluate the performance of various symmetric encryption algorithms—AES, DES, Blowfish, RC4, and ChaCha20—focusing on encryption and decryption efficiency and their impact on Data Manipulation Language (DML) operations, including insertion, selection, and update processes. A payment transaction system was simulated to replicate real-world conditions for assessing DML performance. Performance metrics such as encryption and decryption time were measured under different scenarios involving various database types, encryption key sizes, text lengths, and formats. Custom-developed Python scripts were used to implement the tests, and the results were analyzed using a Microsoft Power BI dashboard for detailed visualization. The findings highlight that RC4 demonstrated the fastest performance across all tested metrics, whereas DES exhibited the longest execution time. AES showed moderate performance compared to the other algorithms. These insights provide valuable guidance for researchers and practitioners on selecting encryption algorithms based on performance requirements, contributing to improved database security and operational efficiency.

The Mediator subunit OsMED23 associates with the histone demethylase OsJMJ703 and the transcription factor OsWOX3A to control grain size and yield in rice

Seed size is one of the important yield traits, and size control is also a fundamental developmental question. The knowledge about the genetic and molecular mechanisms that govern seed size is crucial for improving crop yield. Here, we report that the Mediator subunit OsMED23 associates with the histone demethylase OsJMJ703 and the transcription factor OsWOX3A to control grain size and weight in rice. Loss of function of OsMED23 or OsJMJ703 causes narrow and light grains, while overexpression of OsMED23 or OsJMJ703 results in large and heavy grains. OsMED23 physically interacts with OsJMJ703 and the transcription factor OsWOX3A. OsMED23, OsJMJ703, and OsWOX3A associate with the common promoter regions of two key grain size genes GW2 and OsLAC and repress their transcription by influencing H3K4me3 levels. Field trials demonstrate that overexpression of OsMED23 or OsJMJ703 can significantly increase grain yield. Therefore, our findings identify a mechanism by which the transcriptional repressor complex OsJMJ703-OsMED23-OsWOX3A determines grain size and weight by regulating gene expression and influencing H3K4me3 levels in rice, suggesting that this pathway has a great potential for improving grain yield in key crops.

Optimizing the Efficiency of Series Resonant Half-Bridge Inverters for Induction Heating Applications

This paper reviews the current state of research on half-bridge (HB) inverters used in induction heating power supplies, emphasizing their topological structures, output power control methods, and switching strategies. The study explores various control techniques to regulate low power levels in a series resonant inverter (SRI) configured with an HB structure for induction heating applications. Pulse frequency modulation (PFM) is commonly employed to regulate standard power levels by adjusting the operating frequency relative to the resonant frequency. As the operating frequency increases beyond resonance, the output power decreases. However, in certain scenarios, achieving low power levels necessitates high frequencies, which introduces significant control challenges. To address these issues, it is crucial to develop alternative approaches that ensure efficient power reduction, without compromising system performance. This work evaluates and compares multiple solutions tailored for a high-frequency induction heating system delivering 18 kW at an operating frequency of approximately 100 kHz. The study places particular emphasis on optimizing key component sizing and analyzing inverter losses to enhance overall system efficiency and reliability.

Nomogram construction for overall survival in breast angiosarcoma based on clinicopathological features: a population-based cohort study

BackgroundBreast angiosarcoma (BAS) is a rare, aggressive malignancy with a poor prognosis, often challenging to assess due to its unique biology. This study aimed to develop a nomogram to predict 3- and 5-year overall survival (OS) for BAS patients using key clinicopathological factors.MethodsData from 450 BAS patients diagnosed between 2000 and 2021 were extracted from SEER database. Key variables, including age, tumor size, tumor grade, and distant metastasis status, were identified through univariate and multivariate Cox regression analyses. These factors were incorporated into a nomogram for OS prediction. The model was validated internally and externally using the concordance index (C-index), calibration curves, and decision curve analysis (DCA) to assess its predictive accuracy and clinical utility.ResultsThe nomogram demonstrated good predictive accuracy, with a C-index of 0.68 in the training set and 0.72 in the test set. ROC analysis indicated strong short-term predictive power, with AUC values of 0.81 and 0.75 for 1-year survival in the training and test sets, respectively, though predictive performance declined over time. DCA showed substantial clinical benefit for 12-month predictions, which diminished over longer time frames. The model effectively distinguished high-risk BAS patients and provided individualized survival estimates, supporting its potential use in clinical decision-making.ConclusionThis study presents the first BAS nomogram for OS prediction, showing robust short-term accuracy. The long-term utility is limited by heterogeneity and sample size, highlighting the need for external validation to confirm generalizability and clinical applicability.

Development of a reference database on historical body sizing and digital twins

PurposeHistorical body sizing systems are widely used in reconstructions to reproduce the shape of the human body. However, the systems are less informative than modern standards. The article aims to present a framework for processing historical sizing systems and to generalize 19th century adult female body sizes within one reference anthropometric database.Design/methodology/approachThe study adapted historical data on body sizing to the modern methodology of creating sizing systems. It systematized 248 typical sizes from 33 sources published in North America between 1875 and 1918. The common body measurements were examined by means of correlation analysis. The data was used to introduce new key dimensions and body sizes. Regression models for calculating secondary dimensions were established. A parametric modelling software program was used to generate avatars in accordance with the developed database.FindingsThe developed database presents 111 typical body sizes divided into five constitution and three height groups. The new database agrees with historical body sizes published in Europe. The differences between the developed database and modern standards were confirmed to be in line with changes in the shape of the body during the 19th and the 20th century.Originality/valueThe study provides historical reconstructions and exhibitions with a reliable and comprehensive source of anthropometric data, as well as a diverse range of virtual mannequins.

Secured IoT Data Transmission with Enhanced Integrated Encryption Techniques with Blockchain Technology

The Internet of Things (IoT) revolutionizes how we interact with smart devices. However, ensuring security in IoT systems presents complex challenges due to their centralized architecture and inherent limitations. The increasing attention towards blockchain technology stems from its decentralized structure, transparency, immutable records, and cryptographic hash functions, particularly when combined with the Internet of Things (IoT). Cryptographic hash algorithms are essential to blockchain technology, ensuring secure data transmission by linking private and public keys through mathematical functions. Existing cryptographic algorithms typically produce variable hash values accessible to a single node and support input key sizes up to 128 bytes. However, larger key sizes can reduce transaction accuracy, lower key entropy, decrease throughput, and increase transaction complexity. Conversely, smaller key sizes increase vulnerability to attacks. To overcome these challenges, several advancements have been proposed. A modified key signature scheme extends the key size to 256 bytes, achieving 82.6% accuracy, a time complexity of 1.68 nanoseconds, and a throughput of 15.24 kilobytes per 200 nanoseconds. However, this scheme operates on a single-node basis, limiting accessibility. These advancements aim to bolster security in IoT data transmission by integrating encryption techniques with blockchain technology. The performance of the modified signature scheme is analyzed through transaction parameters such as accuracy, time complexity, throughput, and hash output with cryptographic hash functions like SHA224, SHA256, SHA384, SHA512, MD5, SHA3-224, SHA3-256, SHA3-384, SHA3-512, and ECDSA. From this analysis, the elliptic curve cryptography algorithm gives higher accuracy and throughput with less time complexity.

Fine-particle separation in heavy metal soil remediation using innovative hydrocyclone technology.

Fine-particle separation in heavy metal soil remediation using innovative hydrocyclone technology.

DNA-based cryptosystem integrating Wichmann-Hill and mixed congruence algorithms for enhanced data security

This paper proposes a DNA-based cryptosystem integrating the Wichmann-Hill Algorithm (WHA) and Mixed Congruence Algorithm (MCA) to enhance encryption security. By employing WHA and MCA as hybrid pseudo-random number generators (PRNGs), the system dynamically generates encryption keys, improving randomness and attack resistance. Binary data is encoded into DNA sequences and encrypted using complementary rules, XOR, and permutations. The combined entropy from WHA and MCA ensures highly random outputs, validated by NIST tests, with a key size of ~76 bits for brute-force resistance. Performance evaluations confirm real-time efficiency, while multi-layered encryption enhances cryptanalytic defense. This framework demonstrates the potential of DNA-based cryptography integrated with advanced PRNGs to address modern security challenges and enhance bio-inspired encryption methodologies

MFSA: Migration flamingo search algorithm based trust aware multi-party data sharing in blockchain using hierarchical homomorphic encryption

With the exponential growth of network technology, data sharing becomes more popular and various scholars have carried out comprehensive researches for promoting its development. Multi-party data sharing generally involves multiple participants to access and share data in a trustless environment. To gain the trust of users, it is significant to provide security guarantees that protect user's privacy and obstruct unauthorized access. Therefore, it is essential to guarantee the integrity, confidentiality and trustability of sensitive data and to overcome the risks associated with malicious attacks and unauthorized access. To bridge this gap, this research introduces Migration Flamingo Search Algorithm (MFSA) for multi-party data sharing in the blockchain using hierarchical homomorphic encryption. Initially, data owner (DO) submits a file to cloud through the smart contract (SC). Thereafter, encryption of data files is done employing Hierarchical Multipart Homomorphic Encryption (HMHE). Here, Hierarchical Squeeze Forward Fractional Net (HiSqFFNet) is employed to generate hierarchical key. HiSqFFNet is introduced by combining of Hierarchical Deep Learning Neural Network (HiDeNN) with SqueezeNet. After encryption, an encrypted data is stored in Interplanetary File Systems (IPFS). Then, access request is sent by a data requester (DR) for accessing an encrypted data. The trust manager evaluates trustability of DR and the process is accomplished using MFSA that is presented by incorporating Migration Algorithm (MA) with Flamingo Search Algorithm (FSA). If the trust is acceptable, data from IPFS is decrypted and then, file is delivered to DR. The proposed MFSA is assessed with different evaluation metrics and the evaluation is done using heart disease dataset. Additionally, MFSA has obtained minimal decryption time of 10.548sec, encryption time of 7.978sec, key complexity of 0.727, time complexity of 0.455sec for 100 kb data size with key size = 64.