Decryption Operations Research Articles

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.

In this paper, we propose a dynamic authorizable ciphertext image retrieval scheme based on secure neural network inference that effectively enhances the security of image retrieval while preserving privacy. To ensure the privacy of the original image and enable feature extraction without decryption operations, we employ a secure neural network for feature extraction during the index construction stage of encrypted images. Additionally, we introduce a dynamic authenticatable ciphertext retrieval algorithm to enhance system flexibility and security by enabling users to quickly and flexibly retrieve authorized images. Experimental results demonstrate that our scheme guarantees data image privacy throughout the entire process from upload to retrieval compared to similar literature schemes. Furthermore, our scheme ensures data availability while maintaining security, allowing users to conveniently perform image retrieval operations. Although overall efficiency may not be optimal according to experimental results, our solution satisfies practical application needs in cloud computing environments by providing an efficient and secure image retrieval solution.

Quantum Color Image Encryption: Dual Scrambling Scheme based on DNA Codec and Quantum Arnold Transform

Abstract In the field of Internet, image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of research in the world today. We combine DNA codec with Quantum Arnold Transform (QArT) to propose a new double encryption algorithm for quantum color images. to improve the security and robustness of image encryption.Firstly, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Secondly, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of ”Monkey”, ”Flower” and ”House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average NPCR of RGB three-channel is 99.61%, the average UACI is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

Media Image Transmission and Privacy Protection for Internet of Things Security

Due to their restricted resources, processing capacity, and memory, Internet of Things (IoT) gadgets are susceptible to certain security risks. Encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Enhancing Image Cryptography Performance with Block Left Rotation Operations

In today’s interconnected world, the transmission of both lengthy and concise text messages is ubiquitous across diverse communication platforms. With the proliferation of sensitive and specialized information being exchanged, safeguarding these messages from potential threats such as intruders, abusers, and data hackers becomes imperative. This study introduces an innovative approach aimed at streamlining data protection procedures while concurrently thwarting hacking attempts. At the core of this method lies the utilization of a sophisticated variable content private key designed to facilitate ease of alteration without compromising the integrity of encryption and decryption operations. The pivotal aspect involves leveraging a covert color image to generate this private key, mandating both the sender and receiver to securely retain this image. By employing a selected color image, the private key can seamlessly adapt to match the length of the confidential message. To fortify the level of security, the message is recommended to be segmented into blocks of predetermined sizes, with the sender and receiver jointly establishing these parameters. Subsequently, the bytes within each block are consolidated into a singular vector, which undergoes a left rotation by a predetermined number of bits as specified by the communicating parties. The proposed methodology is empirically validated through the implementation of various color images and text messages. Comparative analyses against existing methods underscore the efficacy and robustness of the proposed approach, substantiating its significant advancements in data protection paradigms.

Secure and Efficient NVM Usage for Embedded Systems Using AES-128 and Huffman Compression

Embedded systems are customized systems designed to meet functionality and specific usage purposes. These systems often grapple with challenges such as power interruptions, limited memory space, and the expectation of a long operational lifespan. It is in this context that Non-Volatile Memory (NVM) plays a vital role. NVM is a type of memory that ensures data persistence even in situations of temporary power loss or signal interruptions. Efficiently utilizing NVM is a critical necessity for the effectiveness and reliability of these systems. Efficient utilization of NVM necessitates the effective management of write and read cycles. Previous studies have demonstrated the efficient utilization of NVM sectors by ensuring that no garbage bytes remain unallocated. Previous studies have demonstrated the efficient utilization of NVM by compressing the data to be written into NVM using the Huffman compression algorithm, and filling sectors without any residual garbage bytes. This paper aspires to advance beyond prior research by integrating the Advanced Encryption Standard (AES-128) block encryption algorithm, aiming to optimize the utilization of NVM to a greater extent. The AES-128 algorithm provides data security with encryption and decryption operations. By applying AES-128 encryption to the data before writing it to the NVM, this work adds a data security layer to the developed software module. Data is divided into blocks using this algorithm. Each block is encrypted independently and then reassembled. The encryption process includes key management, substitution-permutation network layers, and multiple rounds, all of which make substantial contributions to high-level data security. The resulting encrypted data is compressed using the Huffman compression algorithm. This process enhances both the security and efficiency of data storage. The data is written to NVM with maximum efficiency, ensuring that no residual garbage bytes remain, upon the completion of the encryption and compression processes. This study surpasses previous research by enhancing storage security and efficiency through these processes. Moreover, the developed secure and efficient NVM usage, as it fills the NVM with data without leaving any garbage bytes, results in slower processing speed compared to standard NVM usage. Therefore, in future research, interventions with code optimization techniques can be applied to the developed algorithms to enhance write and read speeds.

Revolutionizing the Medical Record Department with a Blockchain-Enhanced Nonlinear Approach to Medical Record Management (MRM) Systems

This work presents a thorough design and construction overview in order to meet the impending issues associated with conducting a groundbreaking project. The centerpiece is a cutting-edge, specially designed for government dentistry institutions, Blockchain-Enhanced Nonlinear Approach to Medical Record Management Systems. The system's goal is to keep patient data safely while offering a user-friendly interface, reliable backup systems, and security. In the first stage, a blockchain-based medical record system will be put into place, and medical staff members will be asked for vital information to be authenticated. The ability to save and manage patient data, including crucial information like patient classifications, is subsequently provided to these verified individuals. The system determines a patient's eligibility to pay medical care costs by intelligently classifying them into groups. For example, BPL holders, elderly residents, and convicts are not required to pay for medical care. This paradigm places a high priority on security, and the article presents the idea of cryptography inside the blockchain. A secret common key is used to assist the encryption and decryption operations that are applied to data. Sensitive medical information is protected by this cryptographic technique, which guarantees the secrecy of discussions between two entities. The data may be retrieved by using this private key, which guarantees the privacy and availability of patient records that are saved.

CVFL: A Chain-like and Verifiable Federated Learning Scheme with Computational Efficiency Based on Lagrange Interpolation Functions

Data privacy and security concerns have attracted significant attention, leading to the frequent occurrence of data silos in deep learning. To address this issue, federated learning (FL) has emerged. However, simple federated learning frameworks still face two security risks during the training process. Firstly, sharing local gradients instead of private datasets among users does not completely eliminate the possibility of data leakage. Secondly, malicious servers could obtain inaccurate aggregation parameters by forging or simplifying the aggregation process, ultimately leading to model training failures. To address these issues and achieve high-performance training models, we have designed a verifiable federated learning scheme called CVFL, where users exist in a serial manner to resist inference attacks and further protect the privacy of user dataset information through serial encryption. We ensure the secure aggregation of models through a verification protocol based on Lagrange interpolation functions. The serial transmission of local gradients effectively reduces the communication burden on cloud servers, and our verification protocol avoids the computational overhead caused by a large number of encryption and decryption operations without sacrificing model accuracy. Experimental results on the MNIST dataset demonstrate that, after 10 epochs of training with 100 users, our solution achieves a model accuracy of 90.63% for MLP architecture under IID data distribution and 87.47% under non-IID data distribution. For CNN architecture, our solution achieves a model accuracy of 96.72% under IID data distribution and 93.53% under non-IID data distribution. Experimental evaluations corroborate the practical performance of the presented scheme with high accuracy and efficiency.

A LIGHTWEIGHT ENCRYPTION ALGORITHM TO SECURE IOT DEVICES

Lightweight cryptography is an effective solution for ensuring confidentiality and privacy in resource-constrained environments, particularly in Internet of Things (IoT) systems. These systems consist of a large number of IoT devices, such as sensor nodes and embedded devices that handle sensitive data and are connected via the Internet, making them susceptible to various types of attacks. Considering the constrained processing capability, memory size, and energy of these devices, this article proposes a lightweight block cipher that aims to provide security for IoT devices, with a focus on achieving efficient software implementation, particularly in devices with 32-bit processors. The structural design of the suggested algorithm is based on Type-2 Generalized Feistel Networks (GFN), which are based on four branches of 32-bit and simple key scheduling. To enhance the diffusion and strength of the GFN structure, a partial permutation layer has been added to the cipher. Moreover, to reduce the implementation cost, a simple round function has been designed using low-cost operations like Addition, XOR, and Rotation. Security of the suggested algorithm has been validated through security analysis, and the results show meeting the criteria for avalanche effect with 50.97% Key Sensitivity and 50.58% Plaintext Sensitivity, and the correlation coefficienttest results indicate a weak correlation between the plaintext and ciphertext, this makes it difficult to retrieve the plaintext from the ciphertext. When designing lightweight algorithms, one of the primary objectives is to enhance performance. To evaluate the algorithm's performance, we tested its execution time, code size, memory consumption, and throughput on real IoT devices, such as ESP8266 and ESP32, as an embedded cryptographic module. Based on the experimental results and security analysis, the suggested algorithm showed a good grade of security and higher performance in encryption and decryption operations than other lightweight algorithms, which makes it capable of providing a framework for securing data in the restricted IoT environment.

A novel package of key generation and integrity validation in symmetric key cryptography

In this paper we presents a new cryptography method on symmetric key Cryptography along with that it will ensure the integrity of the data during the transmission. Here encryption and decryption operation is performed on bit stream of data. It is suitable for any type of text files. This method of encryption is simple and powerful to secure data in network against passive attack like traffic analysis to provide data confidentiality. The Cryptography package deals with generating a simple encryption key by choosing a byte of data in the plaintext. By using that secure encryption key Plain text is converted to cipher text and in the destination Cipher text is converted to plain text by performing basic XOR and other operation. To ensure integrity of data available parity bit methods are applied.

LWED: Lightweight white-box encryption communication system for drones over CARX algorithm

The application field of drones is being broadened with the development of communication and computer technology, but the communication link and data security of drones are facing great threats due to the limited hardware and software resource environment. Attackers can access not only the intermediate data of drone nodes through various means, but even tamper or manipulate their communication environment, when the drone and the control terminal are not trusted and can be regarded as a white-box attack environment. To solve the security problem in drone communication, a lightweight block cipher CARX for drones is proposed and white-boxed for its implementation to cope with the increasingly severe white-box attacks. And over this cryptographic algorithm, a simulated lightweight white-box encryption communication system for drones (LWED) is designed, which can complete the encryption and decryption operations and secure transmission of data and images, etc. The average encryption rate of the 64-bit version of the algorithm is 7.29×10-2Mbps, and the average encryption rate of white-box CARX (WCARX) is 1.18×10-2Mbps, and the average decryption time of the flight status parameters data of the encrypted communication control system is 0.14s. Compared with similar algorithms, the CARX algorithm can guarantee the encryption and decryption efficiency while providing high security and resistance to a variety of attacks, and can fully secure the data during the communication process after being built into the security system of drone.

Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption.

With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data privacy but also enabling meaningful computations on encrypted data; while considerable efforts have been devoted to accelerating expensive homomorphic evaluation in the cloud, little attention has been paid to optimizing encryption and decryption (ENC-DEC) operations on the edge. In this paper, we propose efficient hardware architectures for CKKS-based ENC-DEC accelerators to facilitate computations on the client side. The proposed architectures are configurable to support a wide range of polynomial sizes with multiplicative depths (up to 30 levels) at a 128-bit security guarantee. We evaluate the hardware designs on the Xilinx XCU250 FPGA platform and achieve an average encryption time 23.7× faster than that of the well-known SEAL HE library. By reducing time complexity and improving the hardware utilization of cryptographic algorithms, our configurable CKKS-supported ENC-DEC hardware designs have the potential to greatly accelerate cryptographic processes on the client side in the post-quantum era.

Encrypted Model Predictive Control of a Nonlinear Chemical Process Network

This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously stirred tank reactors that are connected in series and is simulated using Aspen Plus Dynamics. For enhancing system cybersecurity, the Paillier cryptosystem is employed for encryption–decryption operations in the communication channels between the sensor–controller and controller–actuator, establishing a secure network infrastructure. Cryptosystems generally require integer inputs, necessitating a quantization parameter d, for quantization of real-valued signals. We utilize the quantization parameter to quantize process measurements and control inputs before encryption. Through closed-loop simulations under the encrypted LMPC scheme, where the LMPC uses a first-principles nonlinear dynamical model, we examine the effect of the quantization parameter on the performance of the controller and the overall encryption to control the input calculation time. We illustrate that the impact of quantization can outweigh those of plant/model mismatch, showcasing this phenomenon through the implementation of a first-principles-based LMPC on an Aspen Plus Dynamics process model. Based on the findings, we propose a strategy to mitigate the quantization effect on controller performance while maintaining a manageable computational burden on the control input calculation time.

CMAF-IIoT: Chaotic map-based authentication framework for Industrial Internet of Things

The Industrial Internet of Things (IIoT) revolutionizes industrial production using smart devices (SMDs) deployed in IIoT environments. These SMDs collect and transmit information from target fields for analysis and can be controlled remotely. In emergency situations, real-time access to specific SMD information is crucial for prompt actions. However, ensuring secure and reliable communication between users and SMDs in untrusted channels is a significant challenge. Privacy concerns and the limitations of existing authentication frameworks further exacerbate this challenge, necessitating a lightweight cryptography-based authentication framework. This framework is essential to enable secure and reliable communication in the diverse IIoT environment while addressing privacy leakage threats and the computational constraints of IoT devices. Recently, various lightweight cryptography-based authenticated encryption (AE) schemes have been proposed to enable encryption and decryption services for resource-constrained IIoT devices. ASCON, an efficient AE scheme, offers confidentiality, integrity, and authenticity in a single encryption and decryption operation, reducing the number of cryptographic operations required for authentication framework design. This paper presents CMAF-IIoT, a chaotic map and resource-efficient AE scheme (ASCON)-based authentication framework for IIoT, addressing the aforementioned challenges. CMAF-IIoT ensures reliable communication between SMDs and users. The framework begins with user-performed local authentication on their smart devices, followed by the establishment of a session key with the SMD after mutual authentication with the gateway. Using the session key, users securely access real-time information from SMDs deployed in the IIoT environment. The security of CMAF-IIoT is validated through formal and informal security analyses. Additionally, the efficiency of CMAF-IIoT is evaluated in terms of communication, computational, and storage costs. The results indicate that CMAF-IIoT requires [6.67% to 53.33%] low storage cost, [45.13% to 65.87%] low computational cost, and [16.46% to 83.29%] low communication cost compared to contrasted authentication frameworks. These findings highlight the viability of CMAF-IIoT for the IIoT environment, as it provides resource efficiency and enhanced security features.

Dependency-Aware Task Scheduling in TrustZone Empowered Edge Clouds for Makespan Minimization

Task offloading to edge servers has become a promising solution to tackle the computation resource poverty of the end devices. However, the zero-trust edge computing platform is highly challenged by the growing concern on security and privacy. Thus, Trust Execution Environment (TEE), like TrustZone, is advocated to empower edge clouds to enable secure task offloading. To explore TrustZone, the inevitable involvement of data encryption and decryption operations makes existing offloading strategies not applicable any more, especially when the task dependency is considered. In addition, TrustZone has distinguishable task scheduling paradigm as one CPU core does not allow multitask coexist at the same time. Taking the above issues into consideration, we investigate a dependency-aware task offloading problem for makespan minimization in TrustZone empowered edge clouds. By inventing an extended graph to describe the task execution process, we provide a formal statement to the problem and prove its NP-hardness. We then propose a Customized List Scheduling (CLS) based approximate algorithm and theoretically analyze its achievable performance. Extensive testbed based experiment results show that our approximation algorithm can effectively reduce the makespan and significantly outperforms existing state-of-the-art offloading approaches in TrustZone empowered edge clouds.

An Overview of Parallel Symmetric Cipher of Messages

Background:
 Despite significant developments in communications and technology, data protection has established itself as one of the biggest concerns. The data must be encrypted in order to link securely, quickly through web-based technological data transmission. Transforming plain text into ciphered text that cannot be read or changed by malicious people is the process of encryption.
 Materials and Methods:
 In order to maintain the required degree of security, both the cryptanalysis and decryption operations took a significant amount of time. However, in order to cut down on the amount of time required for the encryption and decryption operations to be completed, several researchers implemented the cryptography method in a parallel fashion. The research that has been done on the problem has uncovered several potential answers. Researchers used parallelism to improve the throughput of their algorithms, which allowed them to achieve higher performance levels on the encryption algorithm.
 Results:
 Recent research on parallel encryption techniques has shown that graphics processing units (GPUs) perform better than other parallel platforms when comparing their levels of encryption performance.
 Conclusion:
 To carry out comparison research on the most significant parallel crypto algorithms in terms of data security efficacy, key length, cost, and speed, among other things. This paper reviews various significant parallel algorithms used for data encryption and decryption in all disciplines. However, other criteria must be considered in order to show the trustworthiness of any encryption. Randomness tests are very important to discover and are highlighted in this study.

Grouping Sensors for the Key Distribution of Implicit Certificates in Wireless Sensor Networks

As sensor nodes communicate via wireless channels, information security is essential for wireless sensor networks. Efficient protection mechanisms to ensure data security among nodes are critical. This study developed the bi-directed grouping (top-down grouping (TDG) and bottom-up grouping (BUG)) methods. In this study, we propose a group-based key distribution method, “aggregator-based grouping” (ABG), which combines the advantages of TDG and BUG to address the security issues of nodes. It employs horizontal and vertical searches, which are based on breadth-first and aggregator searches, respectively. A node performs encryption and decryption only when it requires either data aggregation or inter-group communication. The secure aggregation method can be applied to key-grouping management. We compared the proposed method with TDG and BUG using the same number of groups and network structure. For a network with maximum group members of 50 (total sensor nodes = 1000), compared with TDG and BUG, ABG reduced the number of encryption and decryption operations by ~36%. ABG avoids unnecessary encryption and decryption in the network.

CRT-Based Homomorphic Encryption over the Fraction

Homomorphic encryption technology is the holy grail of cryptography and has a wide range of applications in practice. This paper proposes a homomorphic encryption scheme over the fraction based on the Chinese remainder theorem (CRT) Dayan qiuyi rule. This homomorphic scheme performs encryption and decryption operations by forming congruence groups and has homomorphism. The solution in this paper first combines the traditional CRT algorithm with the Dayan qiuyi rule to obtain the CRTF algorithm that can be operated on the fraction field. Finally, in the decryption process, modulo arithmetic is used twice to obtain the correct plaintext components, restored to plaintext by CRTF. The scheme’s security is related to a decisional version of an approximate GCD problem. The proof of theoretical derivation shows that this paper’s homomorphic encryption scheme can realize the homomorphic addition operation in the fraction field. Compared with the CKKS scheme, efficiency is improved.

Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods.

HE-SNA: an efficient cross-platform network alignment scheme from privacy-aware perspective

User alignment across online social network platforms (OSNPs) is a growing concern with the rapid development of internet technology. In reality, users tend to register different accounts on multiple OSNPs, and the network platforms are reluctant to share network structure and user’s information due to business interest and privacy protection, which brings great obstacles to cross-platform user alignment. In view of this, we propose a homomorphic encryption-based social network alignment (HE-SNA) algorithm from the perspective of privacy leakage. Specifically, we first consider the OSNPs as a system containing multiple social networks, that each participant of OSNPs owns part of the network, i.e., a separate private sub-network. Then, encryption, fusion and decryption operations of the alignment information are performed by two third-party servers using HE scheme, which can protect the privacy information of sub-networks effectively. Finally, each sub-network uses the fused alignment information sent back from the third-party server for user alignment. Experimental results show that the HE-SNA method can provide a sum of locally trained models to third-party servers without leaking the privacy of any single sub-network. Moreover, the HE-SNA achieves a promising network alignment performance than only using the structural information and alignment data of single private sub-network while protecting its topology structure information.