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In this article, we study some new properties of twin primes and algorithms for their generation. We find the necessary conditions to generate a pair of twins. These conditions seem to indicate that the conjecture is true, namely, there are infinitely many twin primes. Furthermore, we developed some algorithms that are very useful from a computer science point of view, which can be applied in cryptography and data encryption. [ABSTRACT FROM AUTHOR]

Healthcare institutions generate massive amounts of valuable patient data in the digital age. Finding the right balance between patient privacy and the demand for data-driven medical enhancements is essential. Since data privacy has become increasingly important, robust technologies must be developed to safeguard private data and allow meaningful exploration. This issue was addressed by ShinyAnonymizer, which was first created to anonymize health data. It achieves this by rendering anonymization methods easily available to users. The enhanced version of ShinyAnonymizer, with an essential improvement in performance, is presented in this study. We explain the merging of data analysis, visualization, and privacy-focused statistics paradigms with data anonymization, hashing, and encryption, offering researchers and data analysts an extensive collection of tools for trustworthy data management. [ABSTRACT FROM AUTHOR]

With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs. [ABSTRACT FROM AUTHOR]

This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use. [ABSTRACT FROM AUTHOR]

Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user's device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol's effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. [ABSTRACT FROM AUTHOR]

Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext. [ABSTRACT FROM AUTHOR]

A supply chain (SC) encompasses a network of businesses, individuals, events, data, and resources orchestrating the movement of goods or services from suppliers to customers. Leveraging a blockchain-based platform, smart contracts play a pivotal role in aligning business logic and tracking progress within supply chain activities. Employing two distinct ledgers, namely Hyperledger and Ethereum, introduces challenges in handling the escalating volume of data and addressing the technical expertise gap related to supply chain management (SCM) tools in blockchain technology. Within the domain of blockchain-based SCM, the growing volume of data activities introduces challenges in the efficient regulation of data flow and the assurance of privacy. To tackle these challenges, a straightforward approach is recommended to manage data growth and thwart unauthorized entries or spam attempts within blockchain ledgers. The proposed technique focuses on validating hashes to ensure blockchain integrity. Emphasizing the authentication of sensitive data on the blockchain to bolster SCM, this approach compels applications to shoulder increased accountability. The suggested technique involves converting all data into textual format, implementing code encryption, and establishing permission-based access control. This strategy aims to address inherent weaknesses in blockchain within SCM. The results demonstrate the efficacy of the proposed technique in providing security and privacy for various types of data within SCM. Overall, the approach enhances the robustness of blockchain-based SCM, offering a comprehensive solution to navigate evolving challenges in data management and privacy assurance. [ABSTRACT FROM AUTHOR]

With the rapid development of vehicle electronic communication technology, in-vehicle bus network system communicates with external electronic devices such as mobile phones and OBD II, causing in-vehicle bus networks to face severe network security threats. This study aims to explore the security scheme of in-vehicle bus networks based on a key management system to ensure the confidentiality, integrity, authenticity, and availability of vehicle communication, and innovatively propose a key management system. This key management system uses data encryption and signature algorithms based on the elliptic curve cryptographic domain, which is mainly composed of key generation and key distribution modules. By designing a key life cycle management strategy for In-Vehicle Ethernet and using the digital envelope technique, data encryption and digital signatures are combined to ensure the secure generation and distribution of keys. Experimental simulation results show that the session key negotiation speed of the proposed key management system for In-Vehicle Ethernet in this study is 1.533 ms, which improves the speed by 80.5% compared with the traditional key management system. The key management system proposed in this study improves the real-time information processing efficiency in In-Vehicle Ethernet and lays a solid foundation for the stable development of intelligent connected vehicles. [ABSTRACT FROM AUTHOR]

In this paper, we propose a camera projection approach to enhance the physical layer security of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The data are converted to the new location by the camera projection module in the encryption system, where the 5D hyperchaotic system provides the keys for the camera projection module. The simulated 16QAM CO-OFDM security system over 80 km SSMF is shown to provide a key space of about 9 × 1090 through the five-dimensional (5D) hyperchaotic system, making it impossible for eavesdroppers to obtain valid information, and the peak-to-average power ratio (PAPR) is reduced by about 0.8 dB. [ABSTRACT FROM AUTHOR]

In the face of advancing quantum computing capabilities posing significant threats to current cryptographic protocols, the need for post-quantum cryptography has become increasingly urgent. This paper presents a comprehensive analysis of the performance of various post-quantum cryptographic algorithms specifically applied to digital signatures. It focuses on the implementation and performance analysis of selected algorithms, including CRYSTALS-Dilithium, Falcon, and SPHINCS+, using the liboqs library. Performance tests reveal insights into key pair generation, file signing, and signature verification processes. Comparative tests with the well-known and popular RSA algorithm highlight the trade-offs between security and time efficiency. The results can help to select secure and efficient ciphers for specific 5G/6G services. [ABSTRACT FROM AUTHOR]

Discrete chaotic maps are a mathematical basis for many useful applications. One of the most common is chaos-based pseudorandom number generators (PRNGs), which should be computationally cheap and controllable and possess necessary statistical properties, such as mixing and diffusion. However, chaotic PRNGs have several known shortcomings, e.g., being prone to chaos degeneration, falling in short periods, and having a relatively narrow parameter range. Therefore, it is reasonable to design novel simple chaotic maps to overcome these drawbacks. In this study, we propose a novel fractal chaotic tent map, which is a generalization of the well-known tent map with a fractal function introduced into the right-hand side. We construct and investigate a PRNG based on the proposed map, showing its high level of randomness by applying the NIST statistical test suite. The application of the proposed PRNG to the task of generating surrogate data and a surrogate testing procedure is shown. The experimental results demonstrate that our approach possesses superior accuracy in surrogate testing across three distinct signal types—linear, chaotic, and biological signals—compared to the MATLAB built-in randn() function and PRNGs based on the logistic map and the conventional tent map. Along with surrogate testing, the proposed fractal tent map can be efficiently used in chaos-based communications and data encryption tasks. [ABSTRACT FROM AUTHOR]

Medical data sharing is pivotal in enhancing accessibility and collaboration among healthcare providers, researchers, and institutions, ultimately leading to enhanced patient outcomes and more efficient healthcare delivery. However, due to the sensitive nature of medical information, ensuring both privacy and confidentiality is paramount. Access control-based data sharing methods have been explored to address these issues, but data privacy concerns still remain. Therefore, this paper proposes a secure and privacy-preserving data sharing scheme that achieves an equilibrium between data confidentiality and privacy. By leveraging key aggregate encryption and private set intersection techniques, our scheme ensures secure data sharing while protecting against the exposure of sensitive information related to data. We conduct informal and formal security analyses, including Burrow–Abadi–Needham logic and Scyther, to demonstrate its resilience against potential adversarial attacks. We also implement the execution time for cryptographic operations using multiprecision integer and a rational arithmetic cryptographic library and perform comparative analysis with existing related schemes in terms of security, computational cost, and time complexity. Our findings demonstrate a high level of security and efficiency, demonstrating that the proposed scheme contributes to the field by providing a solution that protects data privacy while enabling secure and flexible sharing of medical data. [ABSTRACT FROM AUTHOR]

A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs. [ABSTRACT FROM AUTHOR]

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a 'data train' and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data. [ABSTRACT FROM AUTHOR]

Developing a security solution for spatial files within today's enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in "data silos" of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution's usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS. [ABSTRACT FROM AUTHOR]

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners' ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user's data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency. [ABSTRACT FROM AUTHOR]

For the secure deployment of network platforms tailored for IoT devices, the encryption of data transmission is equally as crucial as the process of authentication. In this context, we introduce the Secure and Scalable IoT network (SSI) network platform, designed to accommodate a diverse range of IoT devices. It provides scalability and implements effective many-to-many and end-to-end encryption across extensive regions. With the emergence of quantum computing, secure public key exchange mechanisms have become important. Among the various post-quantum cryptography (PQC) algorithms assessed, Nth Degree Truncated Polynomial Ring Units (NTRUs) have emerged as an optimally suited PQC algorithm for IoT devices constrained by limited computational capabilities. We have integrated NTRUs with SSI as a lightweight PQC solution. Moreover, SSI-PQM (Post-Quantum MACsec) enhances the SSI's initial authentication structure to minimize PQC-TLS session attempts and protect the SSI's important configuration information. When applying TLS with PQC for secret key exchange purposes, it was verified that this approach ensures stable performance in IoT environments. Upon the implementation of our proposed SSI-PQM on Raspberry Pi 3B+ based IoT devices, SSI-PQM exhibited acceptable performance at security levels from 80 to 128 and achieved a minimum speed improvement of 161% over RSA at security levels above 160. It can be concluded that SSI-PQM stands out as an effective Zero Trust-based IoT network platform, demonstrating its viability and efficiency in safeguarding data transmission against potential quantum computing threats. [ABSTRACT FROM AUTHOR]

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators' rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

Quantum computing advancements pose security challenges for cryptography. Specifically, Grover's search algorithm affects the reduction in the search complexity of symmetric-key encryption and hash functions. Recent efforts have been made to estimate the complexity of Grover's search and evaluate post-quantum security. In this paper, we propose a depth-optimized quantum circuit implementation for ASCON, including both symmetric-key encryption and hashing algorithms, as a part of the lightweight cryptography standardization by NIST (National Institute of Standards and Technology). As far as we know, this is the first implementation of a quantum circuit for the ASCON AEAD (Authenticated Encryption with Associated Data) scheme, which is a symmetric-key algorithm. Also, our quantum circuit implementation of the ASCON-HASH achieves a reduction of more than 88.9% in the Toffoli depth and more than 80.5% in the full depth compared to the previous work. As per our understanding, the most effective strategy against Grover's search involves minimizing the depth of the quantum circuit for the target cipher. We showcase the optimal Grover's search cost for ASCON and introduce a proposed quantum circuit optimized for depth. Furthermore, we utilize the estimated cost to evaluate post-quantum security strength of ASCON, employing the relevant evaluation criteria and the latest advancements in research. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry. [ABSTRACT FROM AUTHOR]

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study's aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file. [ABSTRACT FROM AUTHOR]

Symmetric cryptography provides the best examples of cryptosystems to be applied in lightweight environments (e.g., IoT). A representative example is the cryptosystem TinyJambu, one of the ten finalists in the NIST Lightweight Cryptography Standardization Project. It is an authentication encryption with associated data scheme that is extremely lightweight and fast. In this work, we analyze the security of TinyJambu from two distinct and non-symmetric points of view: (1) the improvement of the best cryptanalytical attack found in the literature and (2) a randomness analysis of the generated sequences. Concerning item (1), we launched a differential forgery attack with probability 2−65.9487, which was improved considerably compared with previous numerical results. Concerning item (2), we analyzed the degree of randomness of the TinyJambu keystream sequences with a complete and powerful battery of statistical tests. This non-symmetric study shows the weakness of TinyJambu against cryptanalytic attacks as well as the strength of TinyJambu against statistical analysis. [ABSTRACT FROM AUTHOR]

This article provides a general overview of the communication protocols used in the IEC61850 standard for the automation of electrical substations. Specifically, it examines the GOOSE and R-GOOSE protocols, which are used for exchanging various types of information. The article then presents real cases of cyber attacks on the industrial sector, highlighting the importance of addressing cybersecurity in the IEC61850 standard. The text presents security drawbacks of the communication protocols mentioned earlier and briefly explains two algorithms defined in the IEC61850 standard to address them. However, the authors suggest that having only a couple of algorithms may not be sufficient to ensure digital security in substations. This article presents a study on the cryptographic algorithms ChaCha20 and Poly1305. The purpose of the study is to experimentally verify their adaptation to the strict time requirements that GOOSE must meet for their operation. These algorithms can operate independently or in combination, creating an Authenticated Encryption with Associated Data (AEAD) algorithm. Both algorithms were thoroughly reviewed and tested using GOOSE and R-GOOSE frames generated by the S-GoSV software. The computational time required was also observed. The frames were analysed using the Wireshark software. It was concluded that the algorithms are suitable for the communication requirements of electrical substations and can be used as an alternative to the cryptographic algorithms proposed under the IEC61850 standard. [ABSTRACT FROM AUTHOR]

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of intelligent technology, information security and privacy issues have become increasingly prominent. Epidemiological survey data (ESD) research plays a vital role in understanding the laws and trends of disease transmission. However, epidemiological investigations (EI) involve a large amount of privacy-sensitive data which, once leaked, will cause serious harm to individuals and society. Collecting EI data is also a huge task. To solve these problems and meet personalized privacy protection requirements in EIs, we improve the uOUE protocol based on utility-optimized local differential privacy to improve the efficiency and accuracy of data coding. At the same time, aiming at the collection and processing of ESD, a multidimensional epidemiological survey data aggregation scheme based on uOUE is designed. By using Paillier homomorphic encryption and an identity-based signature scheme to further prevent differential attacks and achieve multidimensional data aggregation, the safe, efficient, and accurate aggregation processing of ESD is executed. Through security proof and performance comparison, it is verified that our algorithm meets the requirements of local differential privacy and unbiased estimation. The experimental evaluation results on two data sets show that the algorithm has good practicability and accuracy in ESD collection and provides reliable and effective privacy protection. [ABSTRACT FROM AUTHOR]

BIM collaborative design involves numerous participants from various specialties who create and share vast amounts of design data at different design stages to ensure the efficient transmission of design data between these specialties. It is imperative for the BIM collaborative design platform to guarantee the security of design data and effectively trace any instances of malicious leakage or tampering. Therefore, this paper proposes a traceable ciphertext-policy attribute-based encryption scheme (TCP-ABE) that formulates a dynamic data access control mechanism based on different participants and effectively tracks malicious users in the event of risks such as tampering, theft, and unauthorized access to BIM data. In this scheme, the user's identity information is embedded into their private key as a key component, ensuring that only users who satisfy the access policy can decrypt it. The compromised private key allows for tracing of the user's identity. Additionally, Linear Secret Sharing Scheme (LSSS) is employed as the access structure with the user's attribute set divided into an attribute name set and an attribute value set to protect privacy by concealing the latter within the access policy. Furthermore, the scheme integrates blockchain with cloud storage as a trusted third-party storage mechanism to ensure data integrity. Finally, the TCP-ABE scheme is comprehensively evaluated by comparing its strengths and weaknesses with other algorithms. This evaluation includes a theoretical analysis of functional and computational time overhead aspects, as well as an experimental analysis of initialization time, data encryption time, and data decryption time. The scheme exhibits excellent performance across all stages and encompasses the most comprehensive functionalities, as demonstrated by the comparative analysis and experimental results. [ABSTRACT FROM AUTHOR]

The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme's security against selective ciphertext attacks without relying on random prediction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first ("LENA" text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively. [ABSTRACT FROM AUTHOR]

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance. [ABSTRACT FROM AUTHOR]

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively. [ABSTRACT FROM AUTHOR]

In light of the existing security vulnerabilities within IoT publish–subscribe systems, our study introduces an improved end-to-end encryption approach using conditional proxy re-encryption. This method not only overcomes limitations associated with the reliance on a trusted authority and the challenge of reliably revoking users in previous proxy re-encryption frameworks, but also strengthens data privacy against potential collusion between the broker and subscribers. Through our innovative encryption protocol, unauthorized re-encryption by brokers is effectively prevented, enhancing secure communication between publisher and subscriber. Implemented on HiveMQ, an open-source MQTT platform, our prototype system demonstrates significant enhancements. Comparison to the state-of-the-art end-to-end encryption work, encryption overhead of our scheme is comparable to it, and the decryption cost is approximately half of it. Moreover, our solution significantly improves overall security without compromising the asynchronous communication and decentralized authorization foundational to the publish–subscribe model. [ABSTRACT FROM AUTHOR]

In ciphertext-policy attribute-based encryption, there might be different levels of overlapping in the access policies of different data objects outsourced by the same data owner. This paper proposes a soft set decision-making method and cluster percolation method-based policy clustering by using policy similarity for CP-ABE, aiming to merge the duplicated access policy pieces to reduce repeated computations during the encryption process of corresponding data objects. Firstly, the access policies are clustered using either the soft set decision-making or the cluster percolation method. Secondly, the access policies within the same cluster are integrated for further encryption of corresponding data objects as a whole, thereby preventing redundant computations during the encryption process and thus reducing computational overhead. Theoretical analysis and experimental results demonstrate the feasibility and effectiveness of the proposed approach in this paper. [ABSTRACT FROM AUTHOR]

Applying chameleon hash functions to redactable blockchains is still challenging work. Most redactable blockchain solutions using this technique have potential problems, such as too weak decentralization performance and trapdoors with exposure risks. In addition, quantum computing also threatens the security of blockchain systems. The above two issues imply that the development of redactable blockchains is still constrained, and that quantum-resistance will be requirements for blockchain applications. Therefore, we constructed a chameleon hash function over lattices while utilizing a hierarchical identity mechanism to manage trapdoors and assign edit permissions. This variant of the chameleon hash function can support trapdoor updates and quantum-resistant performance, namely a hierarchical identity-based chameleon hash with revocable subkey (HIBCH-RS). We demonstrated the safety performance of HIBCH-RS by defining its safety concepts of collision resistance. Our HIBCH-RS scheme provides a solution for implementing a redactable blockchain with identity encryption and post-quantum cryptography. Finally, this quantum-resistant redactable blockchain was implemented on the Hyperledger Fabric blockchain platform. [ABSTRACT FROM AUTHOR]

The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods. [ABSTRACT FROM AUTHOR]

Revocable attribute-based encryption (RABE) provides greater flexibility and fine-grained access control for data sharing. However, the revocation process for most RABE schemes today is performed by the cloud storage provider (CSP). Since the CSP is an honest and curious third party, there is no guarantee that the plaintext data corresponding to the new ciphertext after revocation is the same as the original plaintext data. In addition, most attribute-based encryption schemes suffer from issues related to key escrow. To overcome the aforementioned issues, we present an efficient RABE scheme that supports data integrity while also addressing the key escrow issue. We demonstrate the security for our system, which is reduced to the decisional q-parallel bilinear Diffie-Hellman exponent (q-PBDHE) assumption and discrete logarithm (DL) assumption. The performance analysis illustrates that our scheme is efficient. [ABSTRACT FROM AUTHOR]

Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute optimal model parameters without requiring the parties to share their raw data. The new approach also allows for efficient deletion of the data of users who want to leave the group and who wish to have their data deleted. Since the data remain confidential during both the learning and unlearning processes, data owners are more inclined to share the datasets they collect to improve the models, ultimately benefiting all participants. The proposed collaborative computation of linear regression models does not require a trusted third party, thereby avoiding the difficulty of building a robust trust system in the current Internet environment. The proposed scheme does not require encryption to keep the data secret, nor does it require the use of transformations to hide the real data. Instead, our scheme sends only the aggregated data to build a collaborative learning scheme. This makes the scheme more computationally efficient. Currently, almost all homomorphic encryption schemes that support both addition and multiplication operations demand significant computational resources and can only offer computational security. We prove that a malicious party lacks sufficient information to deduce the precise values of another party's original data, thereby preserving the privacy and security of the data exchanges. We also show that the new linear regression learning scheme can be updated incrementally. New datasets can be easily incorporated into the system, and specific data can be removed to refine the linear regression model without the need to recompute from the beginning. [ABSTRACT FROM AUTHOR]

The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well. [ABSTRACT FROM AUTHOR]

To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope. [ABSTRACT FROM AUTHOR]

In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency. [ABSTRACT FROM AUTHOR]

In this article, we present a new method that achieves Shannon's perfect secrecy. To achieve this property, we will introduce the triple XOR cancellation rule. The approach has two execution modes: digital signature and data encryption. We provide perfect secrecy proof of the encryption method. Furthermore, based on our fundamental algorithm, we developed a new strategy for the blockchain system that does not require proof of work (PoW). However, it is a practical mechanism for connecting blocks to the chain. Due to the risk that quantum computers present for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era. We expect our work to have profound implications for the security of communications between mobile devices, the Internet of Things (IoT), and the blockchain. [ABSTRACT FROM AUTHOR]

Using multiple, individual encryption schemes is a well-established method to increase the overall security of encrypted data. These so-called multiple encryption or hybrid schemes have regained traction in the context of public-key cryptography due to the rise of quantum computers, since it allows the combination of well-known classical encryption schemes with novel post-quantum schemes. In this paper, we conduct a survey of the state-of-the-art public-key multiple encryption (M-PKE) schemes. For the first time, we describe the most relevant M-PKE schemes in detail and discuss their security in a unified model, which allows better comparison between the schemes. Hence, we compare the security, efficiency, and complexity of the schemes and offer recommendations for usage based on common use cases. Our survey emphasizes the importance of being deliberate when combining encryption schemes, as small nuances can easily break security. [ABSTRACT FROM AUTHOR]

Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms. [ABSTRACT FROM AUTHOR]

An investigation into the dynamic states and relative intensity noise of laser diodes subjected to double optical feedback has been conducted. We employed modified and improved time-delay rate equations to account for double external optical feedback. The dynamic states and noise of lasers will be investigated using bifurcation diagrams of the output photon number, its temporal variations, and the intensity noise of the laser. This analysis considers feedback strengths due to the double external cavity and their spacing from the front facet of the laser with and without phase due to feedback. The results reveal that considering phase causes significant variations in laser intensity and a phase shift in the temporal variations of the laser output. This results in relative intensity noise suppression and a frequency shift in the intensity noise spectrum. These findings represent new contributions to our understanding of the reliance of lasing frequency shift on the phase due to feedback, regardless of whether feedback originates from a single or double external cavity. We investigated the optimal conditions corresponding to stable dynamic states of the laser with the lowest noise level. Additionally, we identified conditions that result in chaotic dynamics, where the spectrum does not convey information about the laser system. These insights have potential applications in chaotic and secure optical data encryption. [ABSTRACT FROM AUTHOR]

The novelty of this paper lies in its perspective, which underscores the fruitful correlation between biological and computer viruses. In the realm of computer science, the study of theoretical concepts often intersects with practical applications. Computer viruses have many common traits with their biological counterparts. Studying their correlation may enhance our perspective and, ultimately, augment our ability to successfully protect our computer systems and data against viruses. Game theory may be an appropriate tool for establishing the link between biological and computer viruses. In this work, we establish correlations between a well-known computer virus, VirLock, with an equally well-studied biological virus, the bacteriophage ϕ 6 . VirLock is a formidable ransomware that encrypts user files and demands a ransom for data restoration. Drawing a parallel with the biological virus bacteriophage ϕ 6 , we uncover conceptual links like shared attributes and behaviors, as well as useful insights. Following this line of thought, we suggest efficient strategies based on a game theory perspective, which have the potential to address the infections caused by VirLock, and other viruses with analogous behavior. Moreover, we propose mathematical formulations that integrate real-world variables, providing a means to gauge virus severity and design robust defensive strategies and analytics. This interdisciplinary inquiry, fusing game theory, biology, and computer science, advances our understanding of virus behavior, paving the way for the development of effective countermeasures while presenting an alternative viewpoint. Throughout this theoretical exploration, we contribute to the ongoing discourse on computer virus behavior and stimulate new avenues for addressing digital threats. In particular, the formulas and framework developed in this work can facilitate better risk analysis and assessment, and become useful tools in penetration testing analysis, helping companies and organizations enhance their security. [ABSTRACT FROM AUTHOR]

This document is a summary of a book titled "Advancing Complexity Research in Earth Sciences and Geography." The book is a compilation of 20 papers contributed by researchers from around the world, covering various topics such as encryption of digital elevation models, facies heterogeneity, simulation of the snow cover process, exploration of ice elevation change, earthquake and seismic activity, landslide susceptibility, reforestation, coordination between supply and demand of ecosystem services, indoor positioning, public transport flow networks, healthcare facilities, recommender systems for e-retail, globalization, international trade, risk analysis, and quantification of political processes. The book aims to invigorate research in earth sciences and geography and facilitate the further development of complexity science. [Extracted from the article]