Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.

Utilizing Certificateless Cryptography for IoT Device Identity Authentication Protocols in Web3

Traditional methods of identity authentication often rely on centralized architectures,which poses risks of computational overload and single points of failure.We propose a protocol that offers a decentralized approach by distributing authentication services to edge authentication gateways and servers,facilitated by blockchain technology,thus aligning with the decentralized ethos of Web3 infrastructure.Additionally,we enhance device security against physical and cloning attacks by integrating physical unclonable functions with certificateless cryptography,bolstering the integrity of Internet of Thins(IoT)devices within the evolving landscape of the metaverse.To achieve dynamic anonymity and ensure privacy within Web3 environments,we employ fuzzy extractor technology,allowing for updates to pseudonymous identity identifiers while maintaining key consistency.The proposed protocol ensures continuous and secure identity authentication for IoT devices in practical applications,effectively addressing the pressing security concerns inherent in IoT network environments and contributing to the development of robust security infrastructure essential for the proliferation of IoT devices across diverse settings.

Secure SSL/TLS Communication System Based on Quantum Keys

Secure Sockets Layer(SSL)and Transport Layer Security(TLS)protocols facilitates a secure framework for identity authentication,data encryption,and message integrity verification.However,with the recent development in quantum computing technology,the security of conventional key-based SSL/TLS protocols faces vulnerabilities.In this paper,we propose a scheme by integrating the quantum key into the SSL/TLS framework.Furthermore,the application of post-quantum algorithms is used to enhance and complement the existing encryption suites.Experimental results show that the proposed SSL/TLS communication system based on quantum keys exhibits high performance in latency and throughput.Moreover,the proposed system showcases good resilience against quantum attacks.

NTRU_SSS:Anew Method Signcryption Post Quantum Cryptography Based on Shamir’s Secret Sharing

With the advent of quantum computing,numerous efforts have been made to standardize post-quantum cryptosystems with the intention of(eventually)replacing Elliptic Curve Cryptography(ECC)and Rivets-Shamir-Adelman(RSA).A modified version of the traditional N-Th Degree Truncated Polynomial Ring(NTRU)cryptosystem called NTRU Prime has been developed to reduce the attack surface.In this paper,the Signcryption scheme was proposed,and it is most efficient than others since it reduces the complexity and runs the time of the code execution,and at the same time,provides a better security degree since it ensures the integrity of the sent message,confidentiality of the data,forward secrecy when using refreshed parameters for each session.Unforgeability to prevent the man-in-the-middle attack from being active or passive,and non-repudiation when the sender can’t deny the recently sent message.This study aims to create a novel NTRU cryptography algorithm system that takes advantage of the security features of curve fitting operations and the valuable characteristics of chaotic systems.The proposed algorithm combines the(NTRU Prime)and Shamir’s Secret Sharing(SSS)features to improve the security of the NTRU encryption and key generation stages that rely on robust polynomial generation.Based on experimental results and a comparison of the time required for crucial exchange between NTRU-SSS and the original NTRU,this study shows a rise in complexity with a decrease in execution time in the case when compared to the original NTRU.It’s encouraging to see signs that the suggested changes to the NTRU work to increase accuracy and efficiency.

Hardware Architecture for RSA Cryptography Based on Residue Number System

A parallel architecture for efficient hardware implementation of Rivest Shamir Adleman(RSA) cryptography is proposed.Residue number system(RNS) is introduced to realize high parallelism,thus all the elements under the same base are independent of each other and can be computed in parallel.Moreover,a simple and fast base transformation is used to achieve RNS Montgomery modular multiplication algorithm,which facilitates hardware implementation.Based on transport triggered architecture(TTA),the proposed architecture is designed to evaluate the performance and feasibility of the algorithm.With these optimizations,a decryption rate of 106 kbps can be achieved for 1 024-b RSA at the frequency of 100 MHz.

Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system.The pro-posed image cryptography system is based on employing the two-dimensional(2D)chaotic henon map(CHM)in the Discrete Fourier Transform(DFT).The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures.In the proposed DFT-based CHM image cryptography,the confusion is employed using the CHM while the diffu-sion is realized using the DFT.So,the proposed DFT-based CHM image crypto-graphy achieves both confusion and diffusion characteristics.The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the final-ly encrypted image.The decryption procedure follows the inverse procedure of encryption.The proposed DFT-based CHM image cryptography system is exam-ined using a set of security tests like statistical tests,entropy tests,differential tests,and sensitivity tests.The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system.These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.

Preventing Phishing Attack on Voting System Using Visual Cryptography

Phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techniques are crucial. Visual Cryptography (VC) is utilized for efficient voting system authentication to cast votes. VC is one of the most secure approaches for privacy protection as it ensures the confidentiality of the voting system. This paper discusses proposed phishing prevention methods and compares different proposed methods.

Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

Efficient Technique for Image Cryptography Using Sudoku Keys

This paper proposes a cryptographic technique on images based on the Sudoku solution.Sudoku is a number puzzle,which needs applying defined protocols and filling the empty boxes with numbers.Given a small size of numbers as input,solving the sudoku puzzle yields an expanded big size of numbers,which can be used as a key for the Encryption/Decryption of images.In this way,the given small size of numbers can be stored as the prime key,which means the key is compact.A prime key clue in the sudoku puzzle always leads to only one solution,which means the key is always stable.This feature is the background for the paper,where the Sudoku puzzle output can be innovatively introduced in image cryptography.Sudoku solution is expanded to any size image using a sequence of expansion techniques that involve filling of the number matrix,Linear X-Y rotational shifting,and reverse shifting based on a standard zig-zag pattern.The crypto key for an image dictates the details of positions,where the image pixels have to be shuffled.Shuffling is made at two levels,namely pixel and sub-pixel(RGB)levels for an image,with the latter having more effective Encryption.The brought-out technique falls under the Image scrambling method with partial diffusion.Performance metrics are impressive and are given by a Histogram deviation of 0.997,a Correlation coefficient of 10−2 and an NPCR of 99.98%.Hence,it is evident that the image cryptography with the sudoku kept in place is more efficient against Plaintext and Differential attacks.

Quantum Entanglement and Cryptography for Automation and Control of Dynamic Systems

This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems.A dynamic system is a system where the rates of changes of its state variables are not negligible.Quantum entanglement is realized by the Spontaneous Parametric Down-conversion process.Two entangled autonomous systems exhibit correlated behavior without any classical communication in between them due to the quantum entanglement phenomenon.Specifically,the behavior of a system,Bob,at a distance,is correlated with a corresponding system,Alice.In an automation scenario,the"Bob Robot"is entangled with the"Alice Robot"in performing autonomous tasks without any classical connection between them.Quantum cryptography is a capability that allows guaranteed security.Such capabilities can be implemented in control of autonomous mechanical systems where,for instance,an"Alice Autonomous System"can control a"Bob Autonomous System"for applications of automation and robotics.The applications of quantum technologies to mechanical systems,at a scale larger than the atomistic scale,for control and automation,is a novel contribution of this paper.Notably,the feedback control transfer function of an integrated classical dynamic system and a quantum state is proposed.

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major risks.Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server.To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification process.In this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data.The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates.Here Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing.Implementing Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing.Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification.Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity.This system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

Machine learning(ML)and cloud computing have now evolved to the point where they are able to be used effectively.Further improvement,however,is required when both of these technologies are combined to reap maximum bene-fits.A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and,secondly,by preser-ving the privacy of patient data so that it cannot be misused.The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process.Treatment of heart failure may be improved and expedited with this framework.We used the following machine learning algorithms to make predictions:Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbors(KNN),Decision Tree(DT)and Support Vector Machines(SVM).These techniques,combined with cloud computing ser-vices,improved the process of deciding whether to treat a patient with cardiac dis-ease.Using our classifiers,we classified cardiac patients according to their features,which are grouped into single features,combinations of selected fea-tures,and all features.In experiments using all clinical features,machine learning classifiers SVM,DT,and KNN outperformed the rest,whereas in experiments using minimal clinical features,SVM and KNN were the most accurate.Internet of Things(IoT)devices allow family physicians to share diagnostic reports on the cloud in a secure manner.Ring signatures are particularly useful for verifying the integrity of data exchange.Our system keeps the physician's identity confidential from all authorized users,who can still access medical reports publicly.Our pro-posed mechanism has been shown to be both effective and efficient when it comes to obtaining patient reports from cloud storage.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns.Multiple factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision making.The radial basis function network has been used for irrigation prediction.However,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision making.To handle the above mentioned issues,an efficient method for irrigation prediction is used in this work.The factors considered for decision making are soil moisture,temperature,plant height,root depth.The above-mentioned data will be gathered from the sensors that are attached to the cropfield.Sensed data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the cloud.Then the data stored in the cloud will be decrypted key before being given to the deci-sion-making module.Finally,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking module.Thefinal decision regarding the level of water required for cropfields would be taken.Based on this outcome,the water volve opening duration and the level of fertilizers required will be considered.Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey(USGS)database in terms of precision,accuracy,recall,and packet delivery ratio.

DeepGan-Privacy Preserving of HealthCare System Using DL

The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity,despite the ongoing development of cryptography.After looking through the previous research,it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field.Recently,neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images.This revelation came about as a result of current developments.Nevertheless,such an implementation is a technique that is expensive to compute and does not handle the huge variety of different assaults that may be made on pictures.The primary objective of the system that has been described is to provide evidence of a complex framework in which deep neural networks have been applied to improve the efficiency of basic encryption techniques.Our research has led to the development and proposal of an enhanced version of methods that have previously been used to encrypt pictures.Instead,the generative adversarial network(GAN),commonly known as GAN,will serve as the learning network that generates the private key.The transformation domain,which reflects the one-of-a-kind fashion of the private key that is to be formed,is also meant to lead the learning network in the process of actually accomplishing the private key creation procedure.This scheme may be utilized to train an excellent Deep Neural Networks(DNN)model while instantaneously maintaining the confidentiality of training medical images.It was tested by the proposed approach DeepGAN on open-source medical datasets,and three sets of data:The Ultrasonic Brachial Plexus,the Montgomery County Chest X-ray,and the BraTS18.The findings indicate that it is successful in maintaining both performance and privacy,and the findings of the assessment and the findings of the security investigation suggest that the development of suitable generation technologies is capable of generating private keys with a high level of security.

Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as steganography ensures confidentiality,authentication,integrity,and non-repudiation.Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver.Therefore,we present an efficient hybrid security model that provides multifold security assurance.To this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high computations.The modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret data.The enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)algorithm.The experiments are conducted on the images of the USC-SIPI standard image database.The experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

A Low-Cost and High-Performance Cryptosystem Using Tripling-Oriented Elliptic Curve

Developing a high-performance public key cryptosystem is crucial for numerous modern security applications.The Elliptic Curve Cryptosystem(ECC)has performance and resource-saving advantages compared to other types of asymmetric ciphers.However,the sequential design implementation for ECC does not satisfy the current applications’performance requirements.Therefore,several factors should be considered to boost the cryptosystem performance,including the coordinate system,the scalar multiplication algo-rithm,and the elliptic curve form.The tripling-oriented(3DIK)form is imple-mented in this work due to its minimal computational complexity compared to other elliptic curves forms.This experimental study explores the factors playing an important role in ECC performance to determine the best combi-nation that leads to developing high-speed ECC.The proposed cryptosystem uses parallel software implementation to speed up ECC performance.To our knowledge,previous studies have no similar software implementation for 3DIK ECC.Supported by using parallel design,projective coordinates,and a fast scalar multiplication algorithm,the proposed 3DIK ECC improved the speed of the encryption process compared with other counterparts and the usual sequential implementation.The highest performance level for 3DIK ECC was achieved when it was implemented using the Non-Adjacent Form algorithm and homogenous projection.Compared to the costly hardware implementations,the proposed software implementation is cost effective and can be easily adapted to other environments.In addition,the power con-sumption of the proposed ECC is analyzed and compared with other known cryptosystems.thus,the current study presents a detailed overview of the design and implementation of 3DIK ECC.

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Protecting the privacy of data in the multi-cloud is a crucial task.Data mining is a technique that protects the privacy of individual data while mining those data.The most significant task entails obtaining data from numerous remote databases.Mining algorithms can obtain sensitive information once the data is in the data warehouse.Many traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud platform.These strategies are primarily concerned with protecting the privacy of user data.This study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve infinitefields.This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques.Also,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data.Average approximation error,computational cost,anonymizing time,and data loss are considered performance measures.The suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

Nowadays,Wireless Sensor Network(WSN)is a modern technology with a wide range of applications and greatly attractive benefits,for example,self-governing,low expenditure on execution and data communication,long-term function,and unsupervised access to the network.The Internet of Things(IoT)is an attractive,exciting paradigm.By applying communication technologies in sensors and supervising features,WSNs have initiated communication between the IoT devices.Though IoT offers access to the highest amount of information collected through WSNs,it leads to privacy management problems.Hence,this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique(LRECC)to establish a secure IoT structure for preventing,detecting,and mitigating threats.This approach uses the Elliptical Curve Cryptography(ECC)algorithm to generate and distribute security keys.ECC algorithm is a light weight key;thus,it minimizes the routing overhead.Furthermore,the Logistic Regression machine learning technique selects the transmitter based on intelligent results.The main application of this approach is smart cities.This approach provides continuing reliable routing paths with small overheads.In addition,route nodes cooperate with IoT,and it handles the resources proficiently and minimizes the 29.95%delay.