Security Of Scheme Research Articles

BGAS: blockchain-based secure and efficient group key agreement scheme for dynamic vehicular platoon

BGAS: blockchain-based secure and efficient group key agreement scheme for dynamic vehicular platoon

Secure and Lightweight Conditional Privacy-Preserving Identity Authentication Scheme for VANET

Secure and Lightweight Conditional Privacy-Preserving Identity Authentication Scheme for VANET

V2X Application Server and Vehicle Centric Distribution of Commitments for V2V Message Authentication

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95% of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

Prevalence of economic violence among the geriatric population and their knowledge and utilization of social security schemes in Chengalpattu district, Tamil Nadu

ABSTRACT Context: Economic independence is an important factor in the prevention of violence among the elderly. Optimal utilization of various social security schemes in India helps them in achieving financial independence and proper utilization of health care services. However, there is a knowledge and service utilization gap among the elderly in our population. Aim and Objectives: To assess the prevalence of economic violence and awareness and utilization of social security schemes among the geriatric population. Settings and Design: Community-based cross-sectional study conducted in rural and urban field practice areas of the tertiary care center. Methods and Material: A simple random sampling method was adopted. The data were collected using a pilot-tested semistructured questionnaire among 406 participants from three selected villages and wards of Venmalagaram and Maduranthakam during January to July 2023. Statistical Analysis Used: Frequencies and proportions were used for categorical variables, and Chi-square test was applied to test the level of significance. P <0.05 was considered statistically significant. Results: The prevalence of economic violence was found to be 12.1%. Among them, neglect was seen in a major proportion (61.2%). Awareness about the old age pension was seen in 70.7%, followed by Annapurna scheme (17%) and PMVVY (4.9%). The utilization of these schemes was found to be 34.0%, 11.6%, and 2.7%, respectively. Conclusions: The prevalence of economic violence among the geriatric population was in the form of neglect and property misuse. The knowledge and utilization of social security schemes were poor among the elderly.

Physical layer security enhancement scheme for PDM-16QAM system based on seven-dimensional CNN hyperchaotic encryption

The paper presents a secure communication scheme for the optical physical layer based on a seven-dimensional (7-D) Cellular Neural Network (CNN) hyperchaotic encryption. The encryption scheme utilizes a 7-D CNN hyperchaotic system to generate a hyperchaotic sequence as the key source. A portion of this key is selected to encrypt the plaintext image. The proposed scheme has been successfully implemented in a PDM-16QAM system with a data rate of 224 Gbps over a 200 km single-mode fiber (SMF). Experimental results show that authorized users can successfully decrypt the received signal, while eavesdroppers are unable to obtain useful information with a bit error rate (BER) of approximately 0.5. The key space of the scheme is 101792. Through key sensitivity analysis and key space analysis, it is known that the proposed encryption system can effectively resist various attacks by cryptanalysts.

Transnational corporate counterinsurgency in the Colombian conflict and its legacies today

ABSTRACT This study challenges common-sense thinking on internal violent conflicts in two ways: that they are merely domestic, with limited influence from external actors; and that doing business in conflict-ridden places is detrimental to profits. Drawing on empirical research and theoretical lenses from critical security studies and the political ecology of counterinsurgency, the paper explores the transnational dimensions of Colombia’s violent conflict in relation to the extractive sector. Conceptualising two overall generations of counterinsurgency, the article shows how, during the conflict, several transnational corporations, especially those in the extractive and agro-industrial sector, not only established themselves in Colombia and expanded, they also developed their corporate security schemes. Given that energy projects were strategic targets in the guerrilla war, the protection of corporate infrastructure by the state was justified. The paper argues that the internal conflict not only facilitated corporate extractivism, it also provided innovative ways of ensuring corporate security. This ‘social warfare’ approach includes strategic efforts to win hearts and minds, which continue to be pursued today through practices known as ‘corporate social responsibility’ (CSR). The paper relates this to the current challenges to hold economic actors accountable in transitional justice.

Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption

This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.

A Blockchain and Zero Knowledge Proof Based Data Security Transaction Method in Distributed Computing

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

Quantum Security of a Compact Multi-Signature

With the rapid advances in quantum computing, quantum security is now an indispensable property for any cryptographic system. In this paper, we study how to prove the security of a complex cryptographic system in the quantum random oracle model. We first give a variant of Zhandry’s compressed random oracle (CStO), called a compressed quantum random oracle with adaptive special points (CStOs). Then, we extend the on-line extraction technique of Don et al (EUROCRYPT’22) from CStO to CStOs. We also extend the random experiment technique of Liu and Zhandry (CRYPTO’19) for extracting the CStO query that witnesses the future adversarial output. With these preparations, a systematic security proof in the quantum random oracle model can start with a random CStO experiment (that extracts the witness for the future adversarial output) and then converts this game to one involving CStOs. Next, the online extraction technique for CStOs can be applied to extract the witness for any online commitment. With this strategy, we give a security proof of our recent compact multi-signature framework that is converted from any weakly secure linear ID scheme. We also prove the quantum security of our recent lattice realization of this linear ID scheme by iteratively applying the weakly collapsing protocol technique of Liu and Zhandry (CRYPTO 2019). Combining these two results, we obtain the first quantum security proof for a compact multi-signature.

Decentralized CP-ABE Scheme for Enhanced University Data Security Using Blockchain

In studying ample data security in universities, it is essential to protect the privacy and integrity of data. Current schemes utilize blockchain technology for data sharing, where cloud servers are responsible for storing ciphertext and performing partial encryption and decryption calculations. Data encryption technology can effectively prevent hacker attacks and ensure the integrity of data transmission. However, cloud servers that are only partially trusted may tamper with the ciphertext or return incorrect results. To solve these problems, this paper improves the privacy of access policy, attribute revocation and data authenticity of traditional CP-ABE algorithm. A protection scheme for ample public data security in universities is proposed, and a decentralized network structure based on multiple nodes is designed to improve the efficiency of data queries. The simulation results show that the scheme performs well in the scenario where the number of system attributes is moderate and the complexity of the access policy is not high.

A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters

Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

The Security of SSH Protocol Public Key Sharing in the Post-Quantum Era

The security of traditional cryptographic schemes is based on mathematical puzzles that cannot be cracked by current computers. However, the rapid development of quantum computers has the potential to greatly reduce the time and resources required to crack these encryption schemes. Although true quantum supremacy may still be years away, it is imperative to adopt anti-quantum algorithms proactively. This preemptive approach aims to thwart "catch first, decrypt later" attacks, wherein attackers intercept and store encrypted data with the intent of decrypting it once quantum computing becomes sufficiently advanced. This threat is particularly critical for protocols like Secure Shell (SSH), which is widely used for secure communication over unsecured networks. In this paper, we propose an innovative approach to enhance the security of public-key distribution within the SSH protocol. Our method integrates quantum-resistant algorithms to ensure that even with the advent of quantum computing, the confidentiality and integrity of SSH sessions are maintained. We emphasize the importance of using cryptographic protocols that operate over open channels, which, while not necessarily confidential, must be authenticated to prevent tampering. In such scenarios, attackers may be able to intercept and even extract information, but they should not be able to alter the data.

Edge-Computing-Based Scheme for Post-Quantum IoT Security for e-Health

Edge-Computing-Based Scheme for Post-Quantum IoT Security for e-Health

Finding Zero Correlation Linear Hulls Using Quantum Bernstein-vazirani Algorithm

Abstract Facing the threat of quantum computing to cryptography schemes, it is urgent to study the applications of quantum algorithms to cryptanalysis and evaluate the security of cryptographic schemes in a quantum computing environment. To do this, it is necessary to analyze the ability of cryptanalysis methods, such as integral and linear attacks, when combined with quantum algorithms. In this article, we investigate the properties of block ciphers with SPN structure and their security against a quantum adversary and apply the Bernstein-Vazirani algorithm to zero correlation linear attacks. Based on the relation between linear approximates of SPN ciphers and the dual cipher’s linear structures, we proposed a quantum algorithm that can find linear hulls with zero correlation of SPN ciphers. We show the validity of the proposed quantum algorithm and estimate the corresponding computational complexity. Implementing our quantum algorithm only costs polynomial qubits and quantum gates.

Quantization-Based Adaptive Fuzzy Consensus for Multiagent Systems Under Sensor Deception Attacks: A Novel Compensation Mechanism.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of "explosion of complexity" in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G

The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud

Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost.

A Secure and Efficient Authentication Scheme for Large-Scale IoT Devices Based on Zero-Knowledge Proof

A Secure and Efficient Authentication Scheme for Large-Scale IoT Devices Based on Zero-Knowledge Proof

LBCSC: Lattice‐based chameleon signcryption scheme for secure and privacy‐preserving vehicular communications

LBCSC: Lattice‐based chameleon signcryption scheme for secure and privacy‐preserving vehicular communications