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The efficiency of tools used to collect private medical data on people has significantly improved over time. These include implanted, surfacemounted, or encircling devices that form a wireless body area network (WBAN). Although the most recent secure authentication techniques in the industry offer privacy and security, these schemes have a higher time cost for authentication and take longer to complete owing to the restricted computational power of WBAN devices. We provide a novel authentication method depending on the lightweight wearable device scheme for the WBAN environment. Wearable devices are used to capture sensor data from the patient's body in the beginning, after which any redundant data is removed by normalization. The method we suggest for encrypting the data is multi-fractional triphase duplex data encryption (MTDDE) with ant colony optimization (ACO). The method considers not only the security of the data but also the many limitations of sensor nodes, such as battery life, throughput, computing power limitations, and dynamic topology. The thorough research demonstrates that our suggested technique saves computing costs while maintaining security and privacy together with anonymous verification. The suggested system's effectiveness in protecting the privacy and confidentiality of patient health data in WBAN is demonstrated by the simulation model. [ABSTRACT FROM AUTHOR]

The rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments. [ABSTRACT FROM AUTHOR]

Due to the wide variety of distant information transmission, data encryption has become an absolute necessity for practically all data transaction applications. Sensitive data gets uploaded to the cloud every day through many methods. AES algorithm is now the best option for many applications that require security services. Because of this, a lot of research has been carried out to improve the performance and efficiency of the algorithm. This paper's main goal is to propose an improved method for encrypting and decrypting data. As key sharing was a significant problem in the symmetric approach, we generate the key using the ECC and will encrypt and decrypt data using the AES using this key. The suggested algorithm, which is simpler than ECC and solely utilized for key generation-not data encryption or decryption-is more secure than AES because it circumvents the key-sharing issue that plagued AES. [ABSTRACT FROM AUTHOR]

This paper describes Alkaline, a size-reduced version of Kyber, which has recently been announced as a prototype NIST standard for post-quantum public-key cryptography. While not as simple as RSA, I believe that Alkaline can be used in an undergraduate classroom to effectively teach the techniques and principles behind Kyber and post-quantum cryptography in general. Classroom experiences with individual concepts used in Alkaline support this belief. In addition to cryptography, linear algebra and abstract algebra classes would be good candidates for the use of Alkaline. A few exercises suitable for use in these classes are included. [ABSTRACT FROM AUTHOR]

In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption. [ABSTRACT FROM AUTHOR]

The unique infinite self-renewal ability and multidirectional differentiation potential of stem cells provide a strong support for the clinical treatment. In light of the growing demands for stem cell storage, how to ensure personal privacy security and comply with strict ethical supervision requirements is particularly important. In order to solve the problem of low security of traditional encryption algorithm, we proposed a double encryption protection (DEP) algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology. Firstly, we presented the hash value key decomposition algorithm, through the hash value dynamic coding, cyclic shift, conversion calculation to get the key of each subsystem in the built algorithm. Secondly, DEP algorithm for privacy data is realized with two level of encryption. The first level of encryption protection algorithm used AES as the main framework, adding dynamic coding and byte filling based on DNA coding, and carries out dynamic shift of rows and simplified mixing of columns. The second level of encryption protection algorithm conducted random encoding, operation, diffusion and decoding based on the results of our proposed sequence conversion algorithm. Finally, we raised two evaluation indexes, the number of characters change rate (NCCR) and the unified average change intensity of text (UACIT) to measure the sensitivity of encryption algorithms to changes in plain information. The experimental results of using DEP shown that the average values of histogram variance, information entropy, NCCR and UACIT are116.7883, 7.6688, 32.52% and 99.67%, respectively. DEP algorithm has a large key space, high key sensitivity, and enables dynamic encryption of private data in stem cell bank. The encryption scheme provided in this study ensures the security of the private information of stem cell bank in private cloud environment, and also provides a new method for the encryption of similar high confidentiality data. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront. [ABSTRACT FROM AUTHOR]

In view of the security problem of embedded device firmware update, this study proposes a multi-check firmware security update scheme based on hash, symmetric and asymmetric encryption algorithms. In this study, the master key pairs, temporary key pairs, shared key and hash chain are designed to protect firmware update from identity authentication, data encryption, integrity check and other aspects, which can effectively prevent illegal users, firmware tampering, firmware data leakage, replay attack, firmware rollback and other attacks. In this study, the concrete implementation of the security update scheme is carried out. The experiment results show that compared with the ISP(In System Programming) and IAP(In Application Pragramming) technologies without any security protection, the scheme achieves the security protection of the whole process of firmware update at the time cost of about 7% and 11%, it provides a safe and reliable update method for embedded device firmware update. [ABSTRACT FROM AUTHOR]

The objective of this review was to examine the different biometric data encryption techniques or algorithms used in authentication for information security. Consequently, the PRISMA methodology was used to select articles in different databases during the period between 2019 and 2023. The databases used were SCOPUS, SCIELO and GOOGLE ACADEMICO, where when applying the proposed inclusion and exclusion criteria, they were considered 13 articles for this research. In this way, it was possible to notice the need to apply encryption algorithms as a form of attack prevention and aimed at meeting the objectives of information security, the most used being AES (Advanced Encryption Standard) and encryption chaotic. [ABSTRACT FROM AUTHOR]

Threshold cryptography is used to secure a cryptographic secret by dividing it into N shares and storing each claim on a diverse server. Any subgroup of t servers can use the secret without re‐making it. However, an adversary cannot recover the secret by compromising t − 1 servers. Homomorphic encryption operates directly on the ciphertext. In this study, a Threshold multiparty multi‐randomness secure partially homomorphic encryption (TM‐MR‐SPHE) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The model is implemented in Eclipse IDE and AWS Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk (EB) environment. This model is mainly used to secure the patient e‐health details and perform computation on outsourced data. The patient details are encrypted by the algorithm MR‐SPHE and uploaded in AWS (Amazon Web Service) S3 bucket. AWS identity and access management (IAM) creates the users' service and secret shares among multiple users in the EB environment. The proposed model performance is studied by comparing with other partially homomorphic Elgamal, Pailler, and Benaloh. This model achieves data integrity and confidentiality using the threshold multiparty with multi‐randomness secure partially homomorphic encryption. [ABSTRACT FROM AUTHOR]

Electronics-Health Care is one of the new ideas in the healthcare industry. For analyzing the patient's records, doctors need a centralized record of the patients, which gives useful and enormous information. There will be a threat of leaking of confidential e-health data, which will seriously endanger by modifying the health care data of medical and its individual entities. Protection of the confidential data of patients will be a primary constraint in the health care industry. There is a lot of progressiveness in the human services area as well as there is a lot of usage of the information gathered from various sensors and this information is stored in the cloud. As per the existing security algorithms, there is still some security breach and also it won't get satisfied with integrity and consistency while transferring the data from one vendor to others in the health care system. It also causes high spoofing of IP and side channel hackers. With the increase in the use of the internet, there is an increase in hacking also, especially in the health-care sector, e-commerce, military, and Education. Dealing with the huge volume of information in the electronics-healthcare area requires the processing of patient's information day by day and its memory allocation. Cloud is the best option to store these health records in a distributed manner rather than on a single server. So, whatever the information gathered from it will be encoded on block chain technology and an anonymous digital ring signature will be used to authorize the intended user and maintain the privacy of patient cloud-based data distributed Electronics health record system before sending the information to the cloud storage. In this paper, the protection of Personal data of the patient health data is enhanced with block chain and anonymous ring signatures in the e-healthcare cloud; thus making it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

This paper uses the unique biometric features of fingerprints to generate random cryptographic keys. The main aspects of the security of the generated key include the privacy of the fingerprint and the randomness and complexity of the key generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be random from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated keys show the acceptable random nature of the keys. Therefore, the proposed structure for generating a random key can be used in encrypting digital signals with large volumes of data such as image and sound. [ABSTRACT FROM AUTHOR]

In the few years since the Raspberry Pi was released in 2012, countless microcomputers based on the ARM architecture have been introduced. Their small size, high performance relative to their power consumption, and the ability to run the popular Linux operating system make them ideal for a wide range of tasks. Information security is an area of particular importance. Different encryption and encoding algorithms play an important role in almost all areas of information security. However, these algorithms are very computationally intensive, so it is important to investigate which microcomputers can be used for these tasks, and under which trade-offs. The performance of ten different microcomputers is investigated and presented for the application of common symmetric and public-key encryption and decryption, digest creation and message authentication protocols, such as RSA, AES, HMAC, MD5, SHA. Reliable encryption requires the generation of reliable (pseudo)random numbers (Cryptographically Secure Random Numbers, CSRN), and microcomputers based on ARM SoCs usually have hardware implemented (pseudo)random number generators. The applicability of the random number generators of different microcomputers are investigated and presented; test methods are described, and recommendations are made. [ABSTRACT FROM AUTHOR]

Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard data, it is not completely reliable. Various elements, including the particular use case, the type of data, and legal constraints can determine whether it is suitable for keeping private and sensitive data. While there may be benefits, it is important to take into account possible hazards and abide by privacy and security laws. The blockchain itself is secure, but additional applications and layers are not. In terms of security, ML can aid in the development of blockchain applications. Therefore, a critical investigation is required to better understand the function of ML and blockchain in enhancing security. This study examines the current situation, evaluates the articles it contains, and presents an overview of the security issues. Despite their existing limitations, the papers included from 2012 to 2022 highlighted the importance of ML's impact on blockchain security. ML and blockchain can enhance security, but challenges remain; advances such as federated learning and zero-knowledge proofs are important, and future research should focus on privacy and integration with other technologies. [ABSTRACT FROM AUTHOR]

This work aims to solve the specific problem in the Power Internet of Things (PIoT). PIoT is vulnerable to monitoring, tampering, forgery, and other attacks during frequent data interaction under the background of big data, leading to a severe threat to the power grid's Information Security (ISEC). Cryptosystems can solve ISEC problems, such as confidentiality, data integrity, authentication, identity recognition, data control, and nonrepudiation. Thereupon, this work expounds on cryptography from public-key encryption and digital signature and puts forward the model of network information attack. Then, the security of the two cryptograms is certified against the two cyberattack modes. On this basis, an Identity-based Combined Encryption and Signature (IBCES) ensemble scheme is proposed by combining public-key encryption with the digital signature. Finally, the security of the proposed IBCES's encryption and the signature schemes is verified, and the results prove their feasibility. The results show that the proposed IBCEs are effective and feasible, fully meeting the information confidentiality requirements. Additionally, smart grid against Information Security (ISEC) algorithms must comprehensively consider network resources and computing power. This work creatively combines the two cryptosystems. The proposal breaks the traditional key segmentation principle by applying the same key to different cryptosystems and ensures the independent security of the two cryptosystems. The conclusion provides technical support for future research on cryptography. [ABSTRACT FROM AUTHOR]

With the development of cloud computing and its technologies, most institutions and users are interested in storing their sensitive information on third-party servers. Most of the data has been collected using the Internet of Things (IoT) devices which must be saved in the cloud for further analysis. Intermediate access or unauthorized users trying to access sensitive information during the data storage causes data confidentiality, integrity, and privacy-related issues. The IoT devices are integrated with fog computing to carry out the specific computation, latency, and storage over the internet backbone. However, the IoT with Fog computing process ensures the enormous way of managing data security, data latency, privacy, and security are significant issues. In this work, the cryptographic algorithm with the clustering algorithm is developed to manage data security in a distributed environment to overcome this issue. Initially, the cluster heads are identified according to the power probabilistic criteria, and the clustering process is performed for making the data transmission in the fog systems. During data transmission, data security is managed by applying the noise protocol framework encryption process. The encryption technique utilized the various cryptographic functions that effectively manage the privacy and security of the data. The introduced system has been compared with another probabilistic clustering scheme named energy-Efficient Heterogeneous Clustering Algorithm (EEHCA). The discussed process to minimize the intermediate attacks also overcome the node high energy consumption while transmitting the data. [ABSTRACT FROM AUTHOR]

The modern systems that have been based upon the hash function are more suitable compared to the conventional systems; however, the complicated algorithms for the generation of the invertible functions have a high level of time consumption. With the use of the GAs, the key strength is enhanced, which results in ultimately making the entire algorithm sufficient. Initially, the process of the key generation is performed by using the results of n-queen problem that is solved by the genetic algorithm, with the use of a random number generator and through the application of the GA operations. Ultimately, the encryption of the data is performed with the use of the Modified Reverse Encryption Algorithm (MREA). It was noticed that the suggested algorithm provided more sufficient results concerning the key and the strength of security. However, it has lower computational efficiency as compared to the other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an original contribution regarding the improvement of the automatic EOL (End of Line) parallel testing process of electronic control units (ECU) in the automotive industry, by implementing an interwoven parallel testing algorithm to increase the testing quality. The new interwoven parallel test algorithm groups the tests into parallel test groups through which the defect of the pins in the connector is detected and at the same time allows the tests to be performed through the parallel test algorithm of all inputs and outputs. It combines the advantages of reduced test time with the detection of defects on the pins. The last part of the paper highlights the results obtained through the modeling of the interwoven testing algorithm and presents our conclusions that emerge from this case study. [ABSTRACT FROM AUTHOR]

The use of solar energy by converting it and using it into thermal energy - for an efficient and correct use - requires knowing exactly the value of the local solar thermal power. The current paper puts forward a method of measuring solar thermal energy and a method of sizing the active elements used in the solar energy meter using open-source software to simulate finite element thermal transfer phenomena. [ABSTRACT FROM AUTHOR]

Blades are critical components that determine the energy conversion efficiency of an aeroengine. The size of the blade profile and the surface quality must meet the requirements of high strength and reliability. Therefore, precision inspection and quality control are important in manufacturing and assembly. However, the technical characteristics of blades, such as complex profile, thin and distorted structure, high manufacturing accuracy and so on, make the measurement of blades difficult. Existing measurement and inspection methods based on highprecision three-coordinate measurement machine (CMM) and a single optical sensor have obvious technical limitations, which are difficult to satisfy of rapid measurement speed, high accuracy and data integrity of aviation blade profile, and more difficult to achieve accurate measurement of the inlet and exhaust edges. In view of the above problems, this paper studies the measurement method of aviation blade based on the fusion of optical sensors with different principles, and gives full play to the advantages of holographic interferometry and structural scanning, and develops a set of precise and rapid three-dimensional measurement system of aviation blade based on the combination of fringe projection profilometry and conoscopic holography. The measurement experiments and results show that the developed measurement system has high measurement speed and accuracy, and has great potential for popularization and application. [ABSTRACT FROM AUTHOR]

To ensure the stable operation of the distribution network, it is necessary to timely predict and diagnose its faults. In this paper, distribution network faults were briefly analyzed. To address the problem of redundant and incomplete fault data, they were reduced by the rough set (RS), and then the reduced data were predicted based on the back-propagation neural network (BPNN) model. To further improve the prediction performance, an improved particle swarm optimization (IPSO) algorithm was designed to optimize the parameters of the BPNN model and establish the IPSO-BPNN model. Through example analysis, it was found that RS reduction retained the discriminative ability of the data and improved the prediction accuracy. The accuracy of the IPSO-BPNN algorithm was 79.36% when using non-reduced data; the accuracy of the BPNN algorithm and the IPSO-BPNN algorithm was 87.64% and 95.77%, respectively, when using the reduced data. The experimental results demonstrate the reliability of the IPSO-BPNN algorithm for fault data prediction and its applicability in the actual distribution network. [ABSTRACT FROM AUTHOR]

In the working process, how efficiently the robotic arm passes the obstacle nodes has a great impact on its working efficiency. This paper briefly introduced the rapidly exploring random tree (RRT) algorithm and optimized it with the gravitational potential function based on its shortcomings. The B-spline curve was used to smooth the path. Experimental analyses were carried out in two-dimensional and three-dimensional environments. It was found that the improved RRT algorithm had an average running time of 0.39 s and an average path length of 93.96 mm in the two-dimensional environment; taking environment 1 as an example, the average running time and path length of the improved RRT algorithm was 1.03 s and 147.63 mm, respectively, in the three-dimensional environment, after passing through seven path nodes, which were significantly better than the standard RRT algorithm. The results prove the effectiveness of the improved intelligent algorithm for robotic arm path optimization selection. The improved RRT algorithm can be further promoted and applied in practice. [ABSTRACT FROM AUTHOR]

With the development of big data technology, medical data has become increasingly important. It not only contains personal privacy information, but also involves medical security issues. This paper proposes a secure data fitting scheme based on CKKS (Cheon-Kim-Kim-Song) homomorphic encryption algorithm for medical IoT. The scheme encrypts the KGGLE-HDP (Heart Disease Prediction) dataset through CKKS homomorphic encryption, calculates the data's weight and deviation. By using the gradient descent method, it calculates the weight and bias of the data. The experimental results show that under the KAGGLE-HDP dataset,we select the threshold value is 0.7 and the parameter setting is (Poly_modulus_degree, Coeff_mod_bit_sizes, Scale) = (16384; 43, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 43; 23), the number of iteration is 3 and the recognition accuracy of this scheme can achieve 96.7%. The scheme shows that it has a high recognition accuracy and better privacy protection than other data fitting schemes. [ABSTRACT FROM AUTHOR]

Cloud technology provides services for storing and accessing a large amount of data with ease of access and less cost. Sensitive data such as patients' electronic health information should be encrypted before outsourcing into the cloud. Many traditional encryption methods are used for protecting data in the cloud, but unable to perform computation on encrypted data. Homomorphic encryption operates directly on the ciphertext. In this study, a Secure Partially Homomorphic Encryption (SPHE) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The access control policy in the cloud environment is more flexible. An attacker can easily collect sensitive data by abusing the access policy of another user. Therefore, the database privacy is compromised. Creating a role hierarchy and managing the session is difficult in the cloud environment. The above issues motivate us to develop a model which is the integration of the proposed scheme SPHE with role-based user policy. The model is implemented in Eclipse IDE (Integrated Development Environment) and AWS (Amazon Web Service) Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk (EB) environment. This model is particularly used for securing the patient e-health details and performing computation on outsourced data. The patient details are encrypted by the algorithm SPHE and uploaded in AWS S3 (Simple Storage Service) bucket. The users are created by AWS Identity and Access Management (IAM) service and the access level policy is defined based on user roles in EB environment. The proposed model performance is studied by comparing with other partially homomorphic methods Elgamal, Pailler, and Benaloh. This model achieves data integrity and data confidentiality using the role-based user policy with SPHE. [ABSTRACT FROM AUTHOR]

Cryptographic algorithms that can provide both encryption and authentication are increasingly required in modern security architectures and protocols (e.g. TLS v1.3). Many authenticated encryption systems have been proposed in the past few years, which has resulted in several cryptanalysis research work. In this same direction, the National Institute of Standards and Technology (NIST) is coordinating a large effort to find a new standard authenticated encryption algorithm to be used by resource-constrained and limited devices. In this paper, 12 algorithms of the 33 candidates of the Round 2 phase from NIST competition are being benchmarked on a real IoT test-bed. These 33 ciphers implement authenticated encryption with associated data which aims at preserving integrity, privacy and authenticity at the same time. In this work, we ported the 12 algorithms to different hardware platforms (an x86_64 PC, an AVR ATmega128, an MSP430F1611 and the IoT-LAB platform) and make a fair comparison between their performance. We adapted these algorithms to the Contiki operating system to evaluate the latency and efficiency of each algorithm on IoT applications deployed on a national experimental platform which is IoT-LAB. In addition, we used the FELICS-AE benchmark to quantify locally the RAM, execution time and code size of each algorithm. In fact, this work provides practical results of their performance in an IoT scenario which pave the way for further research on other algorithms, platforms or OS. [ABSTRACT FROM AUTHOR]

This work describes a class of Algorithm Substitution Attack (ASA) generically targeting the receiver of a communication between two parties. Our work provides a unified framework that applies to any scheme where a secret key is held by the receiver; in particular, message authentication schemes (MACs), authenticated encryption (AEAD) and public key encryption (PKE). Our unified framework brings together prior work targeting MAC schemes (FSE'19) and AEAD schemes (IMACC'19); we extend prior work by showing that public key encryption may also be targeted. ASAs were initially introduced by Bellare, Paterson and Rogaway in light of revelations concerning mass surveillance, as a novel attack class against the confidentiality of encryption schemes. Such an attack replaces one or more of the regular scheme algorithms with a subverted version that aims to reveal information to an adversary (engaged in mass surveillance), while remaining undetected by users. Previous work looking at ASAs against encryption schemes can be divided into two groups. ASAs against PKE schemes target key generation by creating subverted public keys that allow an adversary to recover the secret key. ASAs against symmetric encryption target the encryption algorithm and leak information through a subliminal channel in the ciphertexts. We present a new class of attack that targets the decryption algorithm of an encryption scheme for symmetric encryption and public key encryption, or the verification algorithm for an authentication scheme. We present a generic framework for subverting a cryptographic scheme between a sender and receiver, and show how a decryption oracle allows a subverter to create a subliminal channel which can be used to leak secret keys. We then show that the generic framework can be applied to authenticated encryption with associated data, message authentication schemes, public key encryption and KEM/DEM constructions. We consider practical considerations and specific conditions that apply for particular schemes, strengthening the generic approach. Furthermore, we show how the hybrid subversion of key generation and decryption algorithms can be used to amplify the effectiveness of our decryption attack. We argue that this attack represents an attractive opportunity for a mass surveillance adversary. Our work serves to refine the ASA model and contributes to a series of papers that raises awareness and understanding about what is possible with ASAs. [ABSTRACT FROM AUTHOR]

This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version. [ABSTRACT FROM AUTHOR]

With the development of big data and the increase of encryption scenarios, it 's difficult to meet the requirements of encryption performance with only software-run encryption methods, and FPGA/ ASIC encryption systems implemented using Verilog/VHDL methods have poor flexibility and difficult maintenance and upgrades problem. In response to the above problems, this paper designed and implemented an AES algorithm encryption system based on heterogeneous reconfigurable computing, including three mainstream modes of AES algorithm ECB, CBC, CTR, each mode realized 128 bit, 192 bit, 256 bit encryption of three key sizes. This paper realized the hardware acceleration of the module based on FPGA, and realized the dynamic switching of different modes and different bit width encryption modules based on the hardware reconfigurable mechanism. By implementing and verifying the system on Intel Stratix 10, the experimental results show that the throughput rates of AES-ECB, AES-CTR and AES-CBC in the system are 116.43 Gbps,60. 34 Gbps, and 4. 32 Gbps, respectively. The ECB mode compared with Intel Xeon E5-2650 V2 CPU and NVIDIA GeForce GTX 1080 GPU achieves 23.18 times and 1.43 times acceleration ratios respectively. Compared with the method just calling the CPU,the overall system has a computational acceleration ratio of 4.72. [ABSTRACT FROM AUTHOR]

It is analyzed that the important part of the fingerprint image is concentrated in one part of the image, and the gray image is the part with useful information in the whole image. Therefore, this paper combines a new fingerprint image encryption algorithm proposed by combining two new one-dimensional chaotic systems, nonlinear functions, and rules with a dynamic "X" model. We propose two new one-dimensional chaotic systems, one is Sin-Cos chaotic system (SCCS) and the other is Cos-Cos chaotic system (CCCS). Firstly, the fingerprint image is binarized and the contour is extracted to get the area of the fingerprint in the image. Then, for the fingerprint area, a round of encryption is carried out by using nonlinear rules and 24 rules of the dynamic X model. Then the whole image is separated into several parts according to the fingerprint position, read into a sequence at one time, and the whole fingerprint image is scrambled and diffused synchronously by using nonlinear rules and functions. The entropy, NPCR, and UACI of the algorithm are very close to the ideal value, and the performance in time efficiency is passable. The algorithm is extremely secure in a large number of test performance analysis results. [ABSTRACT FROM AUTHOR]

Applications relying on the Ethernet IEEE 802.3ba and IEEE802.3b standards require secure data encryption and authentication at extremely high speeds (at least 100 Gbit/s). The Galois/Counter Mode (GCM) is currently considered the de facto standard for hardware high-speed authenticated encryption, although other algorithms have been proposed in the literature such as the Offset Codebook Mode (OCB). The challenge in terms of providing security for high-speed applications is to achieve implementations that explore the parallelism of these algorithms; however, this translates into area cost. In this work, we propose an alternative to GCM and OCB. We show that a combination of the Offset Two-Round authenticated-encryption scheme with the AES block cipher (known as AES-OTR) is exceptionally well suited for exploiting fine-grained parallelism, and can therefore be used to achieve ultra-high-speed data encryption rates. The experiments reported in this paper show that our pipeline-parallel implementation of AES-OTR outperforms the GCM and OCB schemes in terms of throughput per area while using almost half of the logic resources. Our implementation used a Stratix 4 FPGA device as well as several devices from the Virtex family. Implementations of AES-OTR on Stratix 4 used 11kALMs and achieved a throughput of 143.65 Gbit/s. On the Virtex Ultrascale, our design used 31,859 LUTs with a throughput of 204.92 Gbit/s. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input's privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side. [ABSTRACT FROM AUTHOR]

Consensus algorithms are the building block of any decentralized network where the risk of malicious users is high. These algorithms are required to be robust, scalable, and secure in order to operate properly. Localized state-change consensus (LSC) is a consensus algorithm that is specifically designed to handle state-change consensus, where the state value of given data points can dynamically change and the new value needs to be reflected in the system. LSC utilizes a trust measurement mechanism to validate messages and also enforce cooperation among users. Consensus algorithms, and specifically LSC, can be a practical solution for the immutable and secured communication of autonomous systems with limited computational resources. Indeed, distributed autonomous systems are growing rapidly and the integrity of their communication protocols for coordination and planning is still vulnerable because several units are required to act independently and securely. Therefore, this paper proposes a new localized consensus algorithm for immense and highly dynamic environments with validations through reputation values. The proposed solution can be considered as an efficient and practical consensus solution for any paradigms with resourceconstrained devices where a regular encrypted communication method can negatively affect the system performance. [ABSTRACT FROM AUTHOR]

Introduction: Patient initiated, remote cardiac monitoring has proved to be a significant advance in the diagnosis and management of arrhythmias. Further improvements in ease of use and access to results will further improve health outcomes and cost-effectiveness. Here we describe a proof-of-concept evaluation to assess the feasibility of successfully implementing a cloud-based management system using KardiaPro (KP) for remote electrocardiogram (ECG) monitoring to interface into EPIC, an enterprise electronic health record (EHR) system. Methods: The KP management system was embedded using hypertext markup language (HTML) code directly into the EHR. Encrypted credentials and patient data were bundled with an application programming interface key allowing linkage of remote monitoring from patients' smartphones. During the time of implementation, a total of 322 patients and 32 179 ECGs were recorded. Results: The KP-EHR interface provided full functionality, allowing detection, interpretation and documentation of atrial fibrillation (AF), flutter events, ventricular tachycardia, and complete heart block. Our study focused on KP's detection of AF, and 16.7% of tracings were classified as probable AF with only 2.3% of tracings not analyzed by the KP algorithm because of tracings that were too noisy or truncated. Enhanced management was facilitated with clinical information immediately accessible. Blinded physician ECG review validated the KP proprietary algorithm interpretation and ECGs. Conclusions: Direct integration of KP into EHR was successful and practical. It allows for historical, point of care and immediate retrieval of remote ambulatory monitoring data and documentation into the electronic health record. KP EHR integration warrants further study as it has the potential to improve cost-effectiveness and clinical diagnostic value, leading to improvements in delivery of patient care. [ABSTRACT FROM AUTHOR]

Cryptography is a field of study that deals with converting data from a readable to an unreadable format. It can provide secrecy, data integrity, authenticity, and non-repudiation services. Security has become a concern for the community because of the technology's potential use in numerous sectors of any company, market, agency, or governmental body, information. The cryptosystems ensure that data are transported securely and only authorized individuals have access to it. Deeply encrypted data that cannot be deciphered through cryptanalysis are in high demand right now. There are a variety of encryption algorithms that can guarantee the confidentiality of data. For multimedia data, standard symmetric encryption algorithms (AES) can give superior protection. However, using the symmetric key encryption approach on more complicated multimedia data (mainly photos) may result in a computational issue. To address this issue, the AES has been modified to satisfy the high computing requirements due to the complex mathematical operations in MixColumns transformation, which slow down the encryption process. The modified AES uses bit permutation to replace the MixColumns transformation in AES because it is simple to construct and does not require any complex mathematical computation. This research focuses on using the Modified Advanced Encryption Standard (MAES) algorithm with 128 and 256 bit key sizes to encrypt and decrypt image data. The algorithms were implemented using the Python programming language without complex mathematical computation. By comparing the MAES algorithm with the original AES algorithm, the results showed that the MAES requires less encrypting and decryption time with higher efficiency for all file sizes. [ABSTRACT FROM AUTHOR]

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment. [ABSTRACT FROM AUTHOR]

Stage of networking is quintessential task in which security comes into play. Securing these networks which contains confidential digital data that needs to secured will be the agenda of cryptography. Many cryptographic algorithms increment their strengths over parameters like key size, increasing the rounds of iteration and finally using confusion box as S-box as it has best robustness. So, this paper mainly focusses over securing digital data with the help of S-box function over Data Encryption Standard (DES) algorithm. For this, a plain text and key will be given to this DES as it extracts 8x8(64) bit characters from the key and converting them into its corresponding ASCII value and are concatenating to form an 8 value by taking mod16. These will give to 8 S-box in order to generate its corresponding output to make even more secure and also shows dynamic DES gives much result than other crypto methods. The evaluation of this integrated s-box and DES shows much fruitful results over factors like non-linearity, Avalanche criterion, Balance, Robustness to linear cryptanalysis, Robustness to differential cryptanalysis. [ABSTRACT FROM AUTHOR]

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users' transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU's utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack. [ABSTRACT FROM AUTHOR]

Cryptography is a method for secure communication by hiding information with secret keys so that only authorised users can read and process it. Efficient random sequence generators provide robust cipher design for cryptographic applications; further, these sequences are used for data encryption. In this paper, the highly chaotic nature of hybrid chaos maps and neural network is combined to build a random number generator for cryptographic applications. A custom neural network with a user-defined layer transfer function is built to increase the generator's randomness. In this work, the two-hybrid chaotic map's control parameters and iteration value are designed as a layer transfer function to obtain high randomness. Colour image encryption is performed with the extracted sequences and deoxyribonucleic acid encoding technique. Various tests like NIST, attractor test and correlation are applied to the generator to show the degree of randomness. Simulation analysis such as keyspace, key sensitivity, statistical, differential analysis, and chosen-plaintext attack shows the encryption algorithm's strength. [ABSTRACT FROM AUTHOR]