Decryption Scheme Research Articles

Privacy-preserving ADP for secure tracking control of AVRs against unreliable communication

In this study, we developed an encrypted guaranteed-cost tracking control scheme for autonomous vehicles or robots (AVRs), by using the adaptive dynamic programming technique. To construct the tracking dynamics under unreliable communication, the AVR's motion is analyzed. To mitigate information leakage and unauthorized access in vehicular network systems, an encrypted guaranteed-cost policy iteration algorithm is developed, incorporating encryption and decryption schemes between the vehicle and the cloud based on the tracking dynamics. Building on a simplified single-network framework, the Hamilton-Jacobi-Bellman equation is approximately solved, avoiding the complexity of dual-network structures and reducing the computational costs. The input-constrained issue is successfully handled using a non-quadratic value function. Furthermore, the approximate optimal control is verified to stabilize the tracking system. A case study involving an AVR system validates the effectiveness and practicality of the proposed algorithm.

Distributed fusion filtering for multi-rate nonlinear systems with binary measurements under encryption and decryption scheme

Distributed fusion filtering for multi-rate nonlinear systems with binary measurements under encryption and decryption scheme

A Cross-domain Data Sharing Scheme for VANETs Based on Blockchain

With the continuous development of the Vehicular Ad Hoc Network (VANET), cross-domain sharing of vehicle data has become a significant concern. The Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm plays a key role in data sharing as it can achieve “one-to-many” transmission. In this paper, we propose a cross-domain data sharing scheme based on unpaired CP-ABE in VANETs, utilizing the blockchain and InterPlanetary File System (IPFS) system. The blockchain network is composed of trusted authorities (TAs) from different domains. Due to the high-speed movement characteristics of vehicles, we divide vehicle attributes into two categories: static and dynamic. we design a cross-domain data verification contract based on attribute bloom filter (ABF) for decryption testing. Vehicles that pass the test will receive dynamic attribute decryption keys generated by TAs in the data sharing domain to achieve cross-domain access. In addition, we design an outsourced decryption scheme to reduce the computational overhead during vehicle decryption and propose a direct permission revocation mechanism to ensure the flexibility and security of the system. The simulation experiment results show that our scheme optimizes the efficiency of cross-domain data access significantly compared with other approaches.

Efficient Graph Algorithms in Securing Communication Networks

This paper presents three novel encryption and decryption schemes based on graph theory that aim to improve security and error resistance in communication networks. The novelty of this work lies in the application of complete bipartite graphs in two of the schemes and the Cartesian product of graphs in the third, representing a unique approach to cryptographic algorithm development. Unlike traditional cryptographic methods, these graph-based schemes use structural properties of graphs to achieve robust encryption, providing greater resistance to attacks and corruption. Each scheme is illustrated with detailed examples that show how the algorithms can be successfully implemented. The algorithms are written in standard mathematical notation, making them adaptable for machine implementation and scalable for real-world use. The schemes are also rigorously analyzed and compared in terms of their temporal and spatial complexities, using Big O notation. This comprehensive evaluation focuses on their effectiveness, providing valuable insights into their potential for secure communication in modern networks.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Multiple images simultaneous encryption and decryption via deep-learning assisted interferenceless coded aperture correlation holography

Optical encryption based on interferenceless coding aperture correlation holography (I-COACH) is one of the holographic encryption technologies. I-COACH with dual-channel has been employed to encrypt two plaintexts, not only is the encryption capacity limited, but when the two plaintexts are horizontally overlapped, the crosstalk between the two decrypted plaintexts becomes significant. To address these issues, we present an optical multiple images encryption system based on single-channel I-COACH. In our optical encryption system, the multiple images are placed adjacent to each other and encrypted simultaneously through a single-shot I-COACH. The traditional cross-correlation methods fail to decrypt the multiple plaintexts, we propose a decryption scheme based on a novel deep learning framework (M-Net). The multiple plaintexts can be decrypted from a single ciphertext simultaneously, greatly improving the decryption efficiency by our method. Moreover, the crosstalk between the multiple images is avoided. Various experiments are conducted to demonstrate the effectiveness and feasibility of our proposal.

A color image encryption and decryption scheme based on extended DNA coding and fractional-order 5D hyper-chaotic system

With the advancement of information technology, the security of digital images has become increasingly important. To ensure the integrity of images, this paper presents a novel color image encryption and decryption architecture. Firstly, a fractional-order five-dimensional hyper-chaotic system (F5DHS) is proposed and used to generate chaotic sequences for permutation and diffusion processes. Secondly, the extended DNA encoding scheme is employed to generate more DNA encoding rules and four DNA computation methods are used to improve the security of the encryption scheme. Thirdly, a blocked image encryption method is designed to divide the color image into sub-blocks and then determine the DNA encoding, DNA decoding and DNA arithmetic rules for each sub-block based on chaotic sequences. Finally, the results of simulation experiments and security analysis show that the color image encryption algorithm has good encryption performance, a large secret key space, and strong robustness to noise and data loss attacks, indicating that the proposed encryption algorithm can effectively protect the secure communication of digital images.

Efficient and privacy-preserving outsourced unbounded inner product computation in cloud computing

In cloud computing, the current challenge lies in managing massive data, which is a computationally overburdened environment for data users. Outsourced computation can effectively ease the memory and computation pressure on overburdened data storage. We propose an outsourced unbounded decryption scheme in the standard assumption and standard model for large data settings based on inner product computation. Security analysis shows that it can achieve adaptive security. The scheme involves the data owner transmitting encrypted data to a third-party cloud server, which is responsible for computing a significant amount of data. Then the ripe data is handed over to the data user for decryption computation. In addition, there is no need to give the prior bounds of the length of the plaintext vector in advance. This allows for the encryption algorithm to run without determining the length of the input data before the setup phase, that is, our scheme is on the unbounded setting. Through theoretical analysis, the storage overhead and communication cost of the data users remain independent of the ciphertext size. The experimental results indicate that the efficiency and performance are greatly enhanced, about 0.03S for data users at the expense of increased computing time on the cloud server.

Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme

Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme

Attribute-Based Encryption With Reliable Outsourced Decryption in Cloud Computing Using Smart Contract

Outsourcing the heavy decryption computation to a cloud service provider has been a promising solution for a resource-constrained mobile device to deploy an attribute-based encryption scheme. However, the current attribute based encryption with outsourced decryption schemes only enable the mobile device to verify whether the cloud service provider has returned a correct decryption result, they lack a mechanism to enable the cloud service provider to escape from a mobile device's wrong claim if it has returned a correct decryption result. This paper, for the first time, proposes an attribute based encryption with reliable outsourced decryption scheme using the blockchain smart contract. In the proposed scheme, not only can the mobile device verify whether the cloud service provider has returned a correct decryption result, but also the cloud service provider can escape from a wrong claim if the returned decryption result is correct. Moreover, our system achieves the fairness property, which means the cloud service provider can get the reward from the mobile device if and only if it has returned a correct decryption result. Finally, we conduct an implementation to demonstrate that the proposed scheme is practical and efficient.

Blockchain ABE Algorithm in Information System Data Security and Data Delay Encryption

Data security of an information system and data delay encryption are two of the key issues in the digital age. One of the research directions is to integrate blockchain with attribute encryption algorithm, ensuring data security, privacy, reducing decryption complexity and improving the efficiency of decryption. This study makes use of the ABE algorithm of blockchain, combines the advantages of attribute encryption and blockchain technology, and takes the blockchain as the carrier to complete fine-grained scheme for data access control and efficient decryption. By associating the attributes used for data encryption and access permissions, and giving full play to distributed and immutable characteristics of blockchain, secure and reliable data sharing and access control can be realized. The experimental results show that the encryption/decryption time based on blockchain ABE algorithm is 7-15 seconds, and the traditional method needs 14-20 seconds. The ABE algorithm of blockchain has a very important application prospect in the data security of the information system and data delay encryption.

Secure Particle Filtering With Paillier Encryption–Decryption Scheme: Application to Multi-Machine Power Grids

This paper is concerned with the encryption-decryption-based state estimation problem for a class of multi-machine power grids with non-Gaussian noises. For the purposes of security enhancement and data privacy protection, the Paillier encryption-decryption scheme is adopted to map the measurement data into the ciphertext space before being transmitted through the communication network. The aim of this paper is to develop a novel secure particle filter algorithm to cope with the nonlinearity/non-Gaussianity from the system plant and the decrypted signals after the measurement transmission. In particular, a modified likelihood function is proposed to obtain the importance weights where the encryption-decryption process of the measurement data is taken into full consideration. The developed algorithm is applied to multi-machine power grids, and it is demonstrated via simulation studies (on three test scenarios of the IEEE 39-bus power system) that our proposed secure state estimation scheme possesses the desired performance index in terms of security and accuracy.

Novel multiple color images encryption and decryption scheme based on a bit-level extension algorithm

Digital images play a crucial role in modern communication, but the accompanying privacy concerns have become increasingly prominent. In this context, image encryption technology has emerged as a vital means to safeguard the security of digital image information. Over the past few years, a number of effective image encryption schemes have been developed. However, the amalgamation of bit-level encryption techniques with compression algorithms remains underexplored. To leverage the advantages of these two technologies in terms of security and practicality, a novel multiple color images encryption and decryption scheme is devised based on a bit-level extension algorithm. Firstly, four color images are compressed by the discrete wavelet transform (DWT) to reduce transmission and storage consumption. Secondly, to carry out the bit-level scrambling and diffusion operations on pixels with fractional parts in the DWT domain and maintain the decryption image quality as much as possible, a bit-level extension algorithm is developed. Finally, three rounds of scrambling and diffusion operations based on the bit-level extension algorithm are conducted to enhance the randomness of the ciphertext image and conceal the plaintext information. To reinforce the resistance of the presented image encryption and decryption scheme towards the chosen-plaintext and the differential attacks, secret keys are produced with the hash function SHA-512. The presented scheme undergoes a comprehensive analysis including decryption quality, key security, and resistance to the statistical and the differential attacks. The results are: average PSNR = 31.2631 dB, key space reaches 2472, correlation coefficients are almost 0, average entropy = 7.9985 bits, average NPCR = 99.6089%, and average UACI = 33.4658%. Moreover, experimental simulations demonstrate that our solution exhibits a degree of resilience to noise interference and data loss. In summary, our presented multiple color images encryption and decryption scheme based on a bit-level extension algorithm is effective and reliable.

An AI-Enabled Hybrid Lightweight Authentication Scheme for Intelligent IoMT Based Cyber-Physical Systems

In the era of smart healthcare, Internet of Medical Things (IoMT) based Cyber-Physical Systems (CPS) plays an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks to be addressed to maintain credence amidst clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper, we proposed a hybrid lightweight authentication scheme leveraging supervised machine learning (SML) technique followed by Cryptographic Parameter Based Encryption and Decryption (CPBE&D) scheme to ensure the validation of legal patient wearable devices with secure transmission over the wireless communication channel. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overhead with the help of SML technique to predicate and forward authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation upshots of the SML and CPBE&D scheme exhibit extraordinary security features with cost-effective validation of legal patient wearable devices accompanied by worthwhile communication functionalities compared with the predecessor work.

Cryptographic Method to Enhance the Data Security using ElGamal Algorithm and Sumudu Transform

Abstract: Cryptography is the science of using mathematics to encrypt and decrypt data. Cryptography includes two phases: Encryption and Decryption. Encryption is the process of transforming plaintext to ciphertext, whereas decryption is the reverse procedure. Encryption and decryption schemes based on Sumudu Transform are unable to give more security while communicating the information. ElGamal is a public key algorithm that is based on the discrete logarithm problem. The purpose of this study is to introduce a cryptographic method that uses the ElGamal algorithm and Sumudu Transform to improve communication security.

Privacy-Preserving Platooning Control of Vehicular Cyber–Physical Systems With Saturated Inputs

Metaverse allows the physical reality to tightly integrate with the digital universe. As one typical metaverse application, platooning control of vehicular cyber–physical systems has attracted extensive attention as it is beneficial to improve traffic efficiency, driving safety, and emission reduction. However, due to the open nature of wireless communication networks, the transmitted vehicle-to-vehicle (V2V) data packets become exposed to the public and concomitant data leakage can lead to unintended consequences to vehicular platoons. This article is concerned with the privacy-preserving platooning control issue of vehicular cyber–physical systems with input saturations. First, a novel distributed proportional-integral observer is proposed to estimate the full state of each vehicle, where the integral terms with a forgetting factor facilitate to realize the tradeoff between transient performance and steady-state performance for the platoon. Second, sampled-data-based dynamic encryption and decryption schemes, featuring a dynamic private key, are developed such that the encrypted and decrypted V2V data can be kept private to each platoon vehicle. It is then shown that the platooning control problem over a generic communication topology can be cast into the stability issue of an auxiliary dynamic system. Furthermore, sufficient conditions on the existence of the desired observer and controller gains as well as the private key parameter selection are derived to guarantee the desired platoon stability and privacy preservation requirements. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed control method.

A dynamic encryption–decryption scheme for replay attack detection in cyber–physical systems

This paper deals with the replay attack detection problem for a class of cyber–physical systems. A dynamic encryption–decryption scheme is proposed with the purpose of detecting the replay attacks launched by the malicious adversary. First, a new attack model is established to describe the periodic replay attacks that are generated by making use of a series of recorded past measurements. Then, the stealthiness is discussed for the proposed replay attack scheme against the χ2 detector. Subsequently, on basis of the established model, a dynamic encryption–decryption scheme is developed without sacrificing any system performance, guaranteeing 100% detection probability in the occurrence of attacks. Finally, a simulation example is provided to firstly verify the stealthiness of the proposed replay attack and secondly demonstrate the effectiveness of the exploited dynamic encryption–decryption detection algorithm.

Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

Encryption of medical images using a chaotic multi-level scheme

Encryption of medical images is an essential component of the overall security infrastructure of the healthcare industry. Within the current context of digital technology for the storage, transmission, and evaluation of medical data, teleradiology is becoming an increasingly important field. A chaos-based multilevel encryption and decryption scheme is proposed for use with medical images in this research effort. When contrasted with traditional single-level encryption, the logistic chao-based multilevel encryption and decryption were determined to be superior in terms of their effectiveness. The pixel information of the photos is used, along with a permutation method and Xor operations, to construct the secret keys for the images. Metrics such as entropy, correlation measures, the number of pixels change rate, and the unified average changing intensity was utilised during the performance validation process. The proposed strategy is secure against common cryptographic attacks. ADNI Dataset is used for testing and training and contains 2660 MRI images of the human brain.

CRYPTANALYSIS USING LAPLACE TRANSFORM OF ERROR FUNCTION

This paper presents a new cryptographic scheme for encryption and decryption by introducing the Laplace transform of error function. Here we have used the concept of congruence relation and modular arithmetic to nd symmetric key, cipher text and Decryption process. The implementation has been done using Matlab.