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Quantum video encryption is an essential method for ensuring video content security. This paper proposes a quantum video encryption method based on bitplanes and improved Arnold scrambling. Firstly, we design a quantum video representation based on bitplanes (BRQV) and an improved Arnold scrambling to entangle video frames. Then, we use 2D quantum wavelet transforms and an improved logic mapping for frequency-domain encryption. The encryption security analysis reveals that the post-encryption SSIM value is close to 0.015, with an information entropy reaching 7.9541. The circuit complexity analysis shows that the proposed method can realize the video encryption using 16 n 3 + 272 n 2 - 88 n quantum basic gates with the circuit width 2 n + log 2 n + 5 . The best existing quantum video encryption method needs O (2 n n 3) quantum basic gates with the circuit width 3 n + 3 . Simulation results demonstrate that the proposed video encryption method exhibits low correlation coefficients and high efficiency. [ABSTRACT FROM AUTHOR]

Data privacy is becoming more important as the internet of things (IoT) rapidly expands. There are significant consequences for the broad adoption of the internet of things. Homomorphic encryption is one possible solution for protecting sensitive data in the IoT. However, there is a great deal of potential for efficiency gains. In order to get enough privacy and security, it is also crucial. Our homomorphic encryption technique for secure and private optical fibre communication is detailed in this publication. The data owner, fog server, and data consumers' privacy is the primary focus of the proposed architectural design. The performance of this research shows that our FCHE solution improves data security throughout the data storage operation and reduces encryption length. This research also aims to analyse the factors of optical fiber communication cost, storage cost, and computing cost in the context of FCHE. [ABSTRACT FROM AUTHOR]

The object of research is the method and algorithm of arithmetic addition of binomial numbers generated by binary binomial counting systems. The lack of binomial arithmetic, in particular the operation of adding binary binomial numbers, in a certain way prevents their introduction into information systems and the construction of information and communication technologies based on them for combinatorial optimization, generation of combinatorial objects, data compression and encryption. In the framework of the proposed approach, instead of operating with binomial coefficients, only operations with their upper and lower parameters are carried out. At the same time, the weighting coefficients of binary binomial numbers, which are added to each other, are represented in the form of two-component tuples. Taking this into account, this paper presents an algorithm for binomial arithmetic addition using dynamic arrays. The main idea, which is included in the structure of the algorithm of binomial arithmetic addition based on dynamic arrays, is that the transition from a two-dimensional model of summation to a one-dimensional one is carried out. At the same time, only available, existing binomial coefficients are placed in the dynamic array. Accordingly, the search for binomial coefficients equal to or greater than the quantitative equivalent takes place in much smaller areas. In comparison with the algorithm based on matrix models, this quite significantly reduces the amount of time spent when performing the summation operation, and also reduces the requirements for the amount of memory required for placing two-component tuples of the assembly array. In the course of the research, a several-fold decrease in the number of machine cycles required to search for the necessary elements in the dynamic array was practically confirmed. This leads to an increase in the performance of the presented algorithm of binomial arithmetic addition based on dynamic arrays. In turn, this leads to the acceleration of solving information tasks of combinatorial optimization, generation of combinatorial objects, data compression and encryption, for the solution of which the operation of adding binary binomial numbers is used. [ABSTRACT FROM AUTHOR]

Opioid use disorder is a growing public health concern in the United States, causing economic burden and hindered by stigma. New forms of data, including location data, may improve the effectiveness of interventions for preventing and treating opioid use disorder and/or misuse, increase access to treatment and address racial and ethnic disparities. This qualitative study aimed to identify factors that contribute to users' experience with a publicly available location-tracking mobile app - and investigate their privacy and ethical concerns. The study was conducted through two 15-minute interviews within a 48-h time frame. Participants were recruited from a pool of past research participants, Facebook ads, and referrals, and had to meet certain inclusion criteria related to opioid use disorder and/or misuse. The study had a final sample of 30 participants, 15 male and 15 female. The study suggests that a simple onboarding process and convenient experience can enhance participant adherence to the study app and other similar location-based research apps. However, the study also found that participants had concerns about privacy and transparency about locational privacy when sharing their location data. To improve the app, researchers suggest incorporating user behavior earlier in the app development stage. The study also highlights the importance of addressing ethical and privacy concerns such as limiting the types of collected data, incorporating data encryption and retention strategies, giving access to research staff only, and not sharing the data with third-party companies or law enforcement agencies to increase user satisfaction. [ABSTRACT FROM AUTHOR]

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality. [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]

Quantum key distribution (QKD) is one quantum technology that can provide secure encryption keys for data transmission. The secret key rate (SKR) is a core performance indicator in QKD, which directly determines the transmission rate of enciphered data. Here, for the first time, we demonstrate a high-key-rate Gaussian-modulated continuous-variable QKD (CV-QKD) using telecom optical components. The framework of CV-QKD over these components is constructed. Specifically, the high-rate low-noise Gaussian modulation of coherent states is realized by a classical optical IQ modulator. High-baud low-intensity quantum signals are received by an integrated coherent receiver under the shot-noise limit. A series of digital signal processing algorithms are proposed to achieve accurate signal recovery and key distillation. The system can yield a high asymptotic SKR of 10.37 Mbps within 20 km standard telecom fiber, and the secure distance can exceed 100 km. This result confirms the feasibility of CV-QKD with state-of-the-art performance using telecom optical components. Besides, due to the ease of integrating these discrete components, it provides a high-performance and miniaturized QKD solution for the metropolitan quantum network. [ABSTRACT FROM AUTHOR]

With the continuous advancement of System-on-Chip (SoC) technologies, the burgeoning data volumes emphasize the paramount importance of safeguarding data security and integrity. In this study, by leveraging Ascon in conjunction with enhancements to the SHA-1 algorithm, two secure Direct Memory Access (DMA) controllers are designed to facilitate data encryption and comparison, respectively, culminating in the proposal of an SoC architecture featuring dual DMA controllers. Simulation outcomes demonstrate the system's ability to achieve a maximum clock frequency of 120 MHz, offering a throughput rate of up to 3.2 GB/s. The multi-master multi-slave AHB bus matrix within the system operates impeccably, ensuring smooth functionality. Furthermore, the two DMA controllers exhibit independent operation, featuring flexible start-stop capabilities. Notably, they operate harmoniously without conflicts, optimizing the area utilization while adhering to a low power consumption design methodology. The results unequivocally affirm the feasibility of designing a secure SoC integrated with two DMA controllers. This hardware-based approach effectively ensures data security, showcasing promising prospects for real-world applications. [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]

This paper presents an image encryption scheme with data and appearance security, by adopting inter-intra-block scrambling and weighted diffusion. The 2D robust hyper-chaotic map with flexible geometric distribution and rich hyper-chaotic parameter space is employed to generate the key stream for encryption, by considering the characteristics of plaintext. The plain image is first preprocessed by Huffman coding for getting compressed image. Then, the compressed image is divided into four sub-blocks and is further permuted by the designed inter-intra-block scrambling scheme, which can improve the scrambling effect by making the pixel far away from the original adjacent pixels. After that, a weighted diffusion method strongly related to plaintext and key stream is introduced to diffuse the shuffled image to obtain the noise-like cipher image. And in pursuit of higher security, the meaningless noise-like image is embedded into host image to create the visually meaningful cipher image. A series of experiment tests and analyses are carried out to further demonstrate the excellent performances of the encryption scheme. [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]

Polymer‐based room‐temperature phosphorescence (RTP) materials, especially polysaccharide‐based RTP materials, earn sustained attention in the fields of anti‐counterfeiting, data encryption, and optoelectronics owing to their green regeneration, flexibility, and transparency. However, those with both ultralong phosphorescence lifetime and excitation wavelength‐dependent afterglow are rarely reported. Herein, a kind of amorphous RTP material with ultralong lifetime of up to 2.52 s is fabricated by covalently bonding sodium alginate (SA) with arylboronic acid in the aqueous phase. The resulting polymer film exhibits distinguished RTP performance with excitation‐dependent emissions from cyan to green. Specifically, by co‐doping with other fluorescent dyes, further regulation of the afterglow color from cyan to yellowish‐green and near‐white can be achieved through triplet‐to‐singlet Förster resonance energy transfer. In addition, the water‐sensitive properties of hydrogen bonds endow the RTP property of SA‐based materials with water/heat‐responsive characteristics. On account of the color‐tunable and stimuli‐responsive afterglows, these smart materials are successfully applied in data encryption and anti‐counterfeiting. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) refers to a system of interconnected equipment, sensors, and devices deployed within industrial environments whose primary function is to collect and facilitate the exchange of data. IIoT has gained significant traction in several domains, encompassing remote monitoring, equipment management, and condition tracking. The utilization of IIoT in different environments gives rise to apprehensions regarding the security of data. As a result of this, it is imperative to ensure that rigorous safety measures are in place to safeguard the confidentiality of the sensitive information being transmitted. The utilization of elliptic curve cryptography (ECC) and quantum key exchange (QKE) in IIoT networks holds promise for achieving robust and predictable security measures. ECC is a widely adopted type of public-key cryptography due to its efficiency and security. It has gained significant popularity in contemporary systems. QKE is an innovative methodology that originates from the fundamental principles of quantum physics. The system has the ability to generate a secret key that is impervious to hacking attempts, as it leverages natural principles instead of conventional mathematical procedures. Due to this characteristic, quantum key distribution (QKD) is widely recognized as one of the most reliable methods for key exchange. The combination has led to the creation of a novel cryptographic model that integrates elliptic curve cryptography (ECC) and quantum key exchange (QKE) techniques for the generation and distribution of cryptographic keys. The model leads to an increase in the base probability of the Bennett–Brassard protocol (BB84) from 0.5 to 1. Subsequently, the keys that are produced are employed for the encryption of data through a streamlined and adapted variant of the Advanced Encryption Standard (AES), thereby expediting the secure exchange of data while upholding its level of safeguarding. [ABSTRACT FROM AUTHOR]

Reversible optical property changes in lead‐free perovskites have recently received great interest due to their potential applications in smart windows, sensors, data encryption, and various on‐demand devices. However, it is challenging to achieve remarkable color changes in their thin films. Here, methylamine gas (CH3NH2, MA0) induced switchable optical bleaching of bismuth (Bi)‐based perovskite films is demonstrated for the first time. By exposure to an MA0 atmosphere, the color of Cs2AgBiBr6 (CABB) films changes from yellow to transparent, and the color of Cs3Bi2I9 (CBI) films changes from dark red to transparent. More interestingly, the underlying reason is found to be the interactions between MA0 and Bi3+ with the formation of an amorphous liquefied transparent intermediate phase, which is different from that of lead‐based perovskite systems. Moreover, the generality of this approach is demonstrated with other amine gases, including ethylamine (C2H5NH2, EA0) and butylamine (CH3(CH2)3NH2, BA0), and another compound, Cs3Sb2I9, by observing a similar reversible optical bleaching phenomenon. The potential for the application of CABB and CBI films in switchable smart windows is investigated. This study provides valuable insights into the interactions between amine gases and lead‐free perovskites, opening up new possibilities for high‐efficiency optoelectronic and stimuli‐responsive applications of these emerging Bi‐based materials. [ABSTRACT FROM AUTHOR]

With the advancement of content-based medical image retrieval (CBMIR) technology which is used as a convenient assistant for medical diagnosis. However, the potential risk of privacy disclosure in CBMIR remains a concern due to the involvement of patients' sensitive information in medical images. To address this issue, we have proposed an efficient scheme to achieve privacy-preserving medical image retrieval, named PMIR. The primary objective of PMIR is to enhance the accuracy of medical image retrieval while ensuring privacy protection. In PMIR, medical institutions with large repositories of medical images can securely upload to the cloud server with their image data and encryption indexes. Subsequently, users who successfully registered from medical institutions are able to enjoy convenient image retrieval services without revealing their sensitive information and query attributes to the cloud server. The proposed approach emphasizes a privacy-preserving policy mechanism, which empowers users with the right to choose rather than relying solely on service providers. Through rigorous security analysis, it is demonstrated that PMIR can effectively withstand various known security threats. Additionally, experimental results highlight the substantial reduction in communication overhead achieved by PMIR, ultimately providing a seamless and secure search experience for users. [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]

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]

Supply chain management has become increasingly complex, leading to information loss and delayed transmission, exacerbating the bullwhip effect. Furthermore, the need for more reliable information storage, traceability, and accountability has slowed supply chain management. In this context, blockchain technology (BCT) solves the bullwhip effect problem, as it supports distributed networking, information synchronization among nodes, digital encryption, traceable information, and unchangeable block content. This paper proposes using public blockchain to solve the bullwhip effect problem in the supply chain (SC), which has yet to be discussed in published academic research papers. To this end, a framework has been developed to determine the readiness of a supply chain for blockchain adoption. This framework includes a checklist to evaluate the motivation, timing, and appropriate platform for adopting blockchain. The framework has been validated using a case study of personal computer devices, which were affected by the Covid-19 pandemic and resulted in semiconductors supply chain disruption. The study involved polling four executives The study involved polling four executives (of large organizations in Egypt and some Middle Eastern countries) representing different stages in the supply chain, they are retail, distribution, manufacturing, and supply. The results showed that while the executives recognized the benefits of adopting blockchain, they were still determining if it was the right time for adoption and did not see the technology ready for their supply chains. Finally, some public blockchain platforms were introduced to help businesses choose the most suitable platform for adoption. [ABSTRACT FROM AUTHOR]

Encryption chips are specialized integrated circuits that incorporate encryption algorithms for data encryption and decryption, ensuring data confidentiality and security. In China, the domestic SM4 algorithm is commonly utilized, as opposed to the international AES encryption algorithm. These widely implemented encryption standards have been proven to be difficult to crack through crypt analysis methods Currently, power consumption side-channel attacks are the most prevalent method. They involve capturing power consumption data during the encryption process and subsequently recovering the encryption key from this data. The two leading methods are Differential Power Analysis (DPA) and machine learning techniques. DPA does not necessitate prior knowledge but relies heavily on the number of power consumption curves. With only 50 power consumption data points, the accuracy is a mere 80%. Machine learning methods require prior knowledge, achieving an accuracy rate above 95% with only 30 power traces, albeit with training times typically exceeding 15 min. In this paper, a distributed energy analysis attack approach was presented based on Correlation Power Analysis (CPA). The power consumption data was divided into 16 subsets, with each subset corresponding to 8 bytes of the key. By training each subset separately, the 8-byte key's corresponding power consumption data is reduced to only 100 dimensions, resulting in a 76% decrease in cracking time and a 3% improvement in cracking accuracy rate.This article also trains a more complex 256 classification model to directly crack the final key, achieving a success rate of 28% in cracking 128-bit passwords with only 1 power trace [ABSTRACT FROM AUTHOR]

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author summary: Estimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated. [ABSTRACT FROM AUTHOR]

Currently, the size of multimedia data is rising gradually from gigabytes to petabytes, due to the progression of a larger quantity of realistic data. The majority of big data is conveyed via the internet and they were accumulated on cloud servers. Since cloud computing offers internet-oriented services, there were a lot of attackers and malevolent users. They always attempt to deploy the private data of users without any right access. At certain times, they substitute the real data by any counterfeit data. As a result, data protection has turned out to be a noteworthy concern in recent times. This paper aims to establish an optimization-based privacy preservation model for preserving multimedia data by selecting the optimal secret key. Here, the encryption and decryption process is carried out by Improved Blowfish cryptographic technique, where the sensitive data in cloud server is preserved using the optimal key. Optimal key generation is the significant procedure to ensure the objectives of integrity and confidentiality. Likewise, data restoration is the inverse process of sanitization (decryption). In both the cases, key generation remains a major aspect, which is optimally chosen by a novel hybrid algorithm termed as "Clan based Crow Search with Adaptive Awareness probability (CCS-AAP). Finally, an analysis is carried out to validate the improvement of the proposed method. [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]

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]

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]

The principal constituent of a block cipher in symmetric-key cryptography is the Boolean function, determining the substitution box (S-box). Block ciphers rely totally on S-boxes with excellent nonlinearity and upright cryptographic structures. In AES, an 8 × 8 S-box is a 16 × 16 lookup table over the Galois field GF 2 8 , that occupies 8 × 28 bytes storage of computer memory. By using traditional method to construct a 24 × 24 S-box over Galois field GF 2 24 , which lodges a storage memory of 24 × 224 bytes in traditional sense. Thus, the memory storage does not support a 24 × 24 S-box over a very larger order Galois field like GF 2 24 . A resolute of this difficulty is possibly coming out from the algebraic structure of the commutative finite chain ring F q [ x ] < x k > = ∑ i = 0 k - 1 x i F q. In this study, a subgroup of the multiplicative group of units of the chain ring F 2 [ x ] < x 24 > = ∑ i = 0 23 x i F 2 is considered to construct a 24 × 24 S-box that occupy just 24 × 28 bits storage memory of computer. The proposed S-box has a substantial potential to create confusion during substitution phase of the color image enciphering algorithm. While for the permutation component of the algorithm, DNA transform is applied for creating diffusion in the pixels of the color image. The proposed RGB image encryption attains the standard optimum level when compared it to the DNA and chaos-based image encryption techniques. [ABSTRACT FROM AUTHOR]

After the tremendous development and fast growth in communication systems and electronic devices, information security and preservation have become one of the most important procedures to be considered when transferring data between two parties. Encryption and data hiding are two of the most important techniques to protect the information sent over networks and the Internet. In this paper, a new technique of encryption and concealment is used through the following steps: First, the secret information is encrypted by using developed DNA to get (S1), second, by scrambling the encrypted data, the (S1) is diffused by using a zigzag method to get (S2), third, re-obfuscation the (S2) by using developed zigzag method through using Tinkerbell technique to get (S3), fourth, perform XoR operation between the (S3) and the Tinkerbell technique to get (S4), and fifth, the (S4) is hidden in the random locations of the cover image based on the LSB technique and the Tinkerbell technology, where the Tinkerbell has incredibly excellent characteristics and used to generate random sequence numbers twice with different initial and parameters value, once for the encryption process for scrambling secret information and once for the hiding process for determining hidden locations. Several metrics were used to evaluate the system's efficiency: histogram, PSNR, MSE, mean absolute error, bit error rate, encryption quality, Unified Average Changing Intensity, hamming distance, entropy, and correlation coefficient. The experimental results display that the suggested method has a good performance and reliability, high security, and greater embedding capacity and ensures higher image quality; also, the proposed method presented preferable outcomes when compared to other approaches. [ABSTRACT FROM AUTHOR]

Cloud storage is the ideal solution for outsourcing big data since the cloud can store a large amount of data. Cloud storage, on the other hand, raises additional problems about data duplication, fine-grained access control, and privacy, all of these factors are crucial for cloud large data storage. Data duplication approaches based on encrypted data schemes now available do not provide for fine-grained access control. This paper proposes a secure framework for managing data using rapid asymmetric maximum based dynamic size chunking and fuzzy logic for deduplication. Chunking, fingerprinting, hashing, and writing are the four main process of the proposed method. Initially, chunking is done to split the files into chunks. Rapid Asymmetric Maximum (RAM) based Dynamic Size Chunking (DSC) is used in the proposed method. These chunked files are then fingerprinted using hashing process for ensuring data authentication. Then B-tree indexing approach is used in the proposed method in order to keep the fingerprinted in an organized state. General Type2-Fuzzy logic is using Ant Lion Optimization (ALO) is used for detecting duplicate files in the documents. In the cloud storage platform, only non-duplicate documents are safely kept. The Triple Data Encryption Standard is used to do a security study before outsourcing non-duplicate data to a third-party cloud server. The total computation time of the proposed technique is 0.4 s in the inline phase and 0.04 s in the offline phase, and the deduplication ratio is 95% in the inline phase and 90% in the offline phase. This proposed deduplication approach requires less storage, which reduces memory use and processing time. [ABSTRACT FROM AUTHOR]

Metasurfaces composed of subwavelength structure arrays provide a diverse and powerful platform for generation of complicated electromagnetic (EM) fields and wavefronts manipulation and have triggered an unprecedented research upsurge in electromagnetism with the development of computer‐generated holography (CGH). Different from conventional scalar holograms only containing amplitude and phase properties, a vectorial hologram that benefits from the maneuverability of polarization of EM fields exhibits more immeasurable application potential and is simultaneously regarded as an urgent requirement. In this work, assisted by single completely spin‐decoupled meta‐particles, which perfectly combine the functions of simultaneous and independent amplitude and phase modulation of orthogonal polarization states containing left‐ and right‐handed circularly polarized (LCP&RCP) components, a vectorial hologram induced through eigenstates degeneracy with the ability of encoding arbitrary polarization is proposed in the microwave region. As the proof of concept, eight distinct holograms with specified polarizations and six‐channel polarization‐multiplexed hologram display are fabricated and characterized, further showcaing the versatility of the approach for the customization of arbitraty polarization and integration of reconstructed patterns. With the advantages of multi‐controllability and ultra‐compactness, the constructed monolayer completely spin‐decoupled paradigm for vectorial regulation may find prospective application aspects in spin detection, target perception, multi‐channel data encryption, and many other related fields. [ABSTRACT FROM AUTHOR]

Nowadays the cloud computing environment is widely utilized for transmitting and receiving data securely. Inorder to secure the data the encryption method is used but still due to some limitations the security process is diminished. Therefore, this paper proposes a new algorithm to provide better security while transmitting data through the network. At first, the sensitivity of data is determined using a lightweight convolutional neural network (LWCNN) model which is used to categorize the unclassified data into two categories normal sensitive data and highly sensitive data. After determining the level of data sensitivity, the encryption process is performed further. The efficient hash function-based duplication detection approach is employed to maintain confidential information before outsourcing it to a cloud server. Subsequently, the ideal keys are generated for each data based on its sensitivity level using the proposed fuzzy tuna swarm (FTS) algorithm. Finally, the data is encrypted by converting plain text into ciphertext which is only visible to authorized users. The experimental results show that the LWCNN model utilized for data sensitivity classification achieved 94% accuracy and the FTS algorithm proposed for optimal key generation took much less communication time of about 1800 µs than other compared techniques. [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]

Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while playing. The present work aims to fill the gap in the existing literature by showcasing the power of learning analytics methods to reveal and represent students' behavior when participating in a computer-supported educational escape room. Specifically, we make use of sequence mining methods to analyze the temporal and sequential aspects of the activities carried out by students during these novel educational games. We further use clustering to identify different player profiles according to the sequential unfolding of students' actions and analyze how these profiles relate to knowledge acquisition. Our results show that students' behavior differed significantly in their use of hints in the escape room and resulted in differences in their knowledge acquisition levels. [ABSTRACT FROM AUTHOR]

With the demand for the diversity and security of information deliveries increasing, advanced security tag for data encryption has attracted great interest from the scientific community. It is promising to achieve composite dynamic information storage by the combination of multisource stimuli‐responsive mechanism. Here, mechano‐ and chemochromism cholesteric liquid crystal elastomers (CLCEs) are prepared by introducing a rhodamine lactam‐based functional trigger by two different strategies, one of which is one‐pot method and another is postfunctionalization method. The performances of the CLCEs from two strategies are investigated, which are demonstrated to be effective methods in endowing material with new function, and suitable for different application scenarios. The composite material combines the force‐induced structural periodicity variation of CLCE and the pigmentary and fluorescent properties changes of the functional trigger caused by the chemical environment. The orthogonal construction of dynamic structural color pattern and reversible pigmentary and fluorescent colors pattern enable the information in the CLCE to display double encryption mode, which exhibits great potential in the information storage and anti‐counterfeiting. [ABSTRACT FROM AUTHOR]

Electronic payment (E-payment) systems rely heavily on secure communication, typically achieved through encryption processes in cryptography. Traditional cryptography often relies on complex mathematical problems for encryption, such as difficult decompositions or extracting discrete logarithms. With the development of quantum computing, the security of these classical encryption methods is under threat. To address this challenge, quantum E-payment protocols have emerged as a potential solution. However, the practical quantum E-payment protocols are limited due to the channel transmission efficiency decreasing exponentially with distance. This is further constrained by the Pirandola–Laurenza–Ottaviani–Banchi (PLOB) bound. To address this challenge, we propose a twin-field quantum encryption protocol for E-payment based on blockchain. In our protocol, we propose a twin-field quantum encryption method to authenticate identity and detect eavesdropping simultaneously. It not only eliminates side-channel attacks on the detectors but also has the potential to break the PLOB bound. Our security analysis shows that our E-payment protocol exhibits security properties such as blindness, unforgeability, and undeniability. Furthermore, our protocol demonstrates withstand against common inside and outside attacks. [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]

The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology's rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user's privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2CC) mechanism. Finally, in the data retrieval phase, the user's authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models. [ABSTRACT FROM AUTHOR]

One of the pioneer and important fields in the computer science area is cloud computing. The data within cloud computing are usually transformed to it from local storage; therefore, the security of this data is an important issue. To solve this data security issue, it is important that cloud service providers (CSPs) store encrypted versions of user data. Before transmitting data to the cloud provider, it was encrypted using traditional encryption schemes. Nevertheless, for these schemes, the private key must be provided to the server to be used for the decryption on the other side before any calculations, yielding a security risk and issue for the cloud data. Homomorphic encryption provides a capable solution to this issue since it enables calculations on encrypted data with no need to be decrypted and the private encryption key is not compromised. A new fully homomorphic encryption scheme to protect cloud data is proposed in this paper, it is called NAZUZ. The NAZUZ scheme is based on prime modular operations and encrypts messages by operating on each character without converting them to binary. NAZUZ security relies on the difficulty of factoring large integer numbers and introduces noise complexity to the plaintext through the number of CSP users. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is an emerging field in the IT sector due to its prospective, progress and incredible impact on the development of that sector. CC is a centralized and distributed network comprised of interrelated and interconnected systems, and the whole universe uses the cloud for the ultimate portion of data transmission. However, the cloud cannot provide security assurance, which is one of the main drawbacks of the cloud. Blockchain is the fascinating massive attention in the cloud field because of its security features. It consists of a chain of several blocks composed of multiple transactions. These data available in blocks are encrypted and cannot be altered by any in the world. Several emerging applications are affected by insurance companies, anomalies, thieves, and intruders fetching individual data and disturbing the user's routine. This paper develops a blockchain-based modified infinite chaotic elliptic cryptography (MICEC) to improve and tighten the security in the cloud. It contains three stages: protection of authentication, ownership protection, and identity mapping validation. At first, MICEC is employed for an authentication process that combines the infinite elliptic curve cryptography and modified chaotic neural network for pair of key generation and data encryption. In addition, the hash function is generated with the aid of the improved message digest 5 algorithm. Second, the improved Message Content Recommendation Algorithm with Latent Dirichlet Allocation is used to compute the score values. At last, cosine similarity matching criteria is computed in the validation based on the digests. The proposed work is simulated using the JAVA programming language with NetBeans IDE. The performance is evaluated and compared to other methods. [ABSTRACT FROM AUTHOR]

Multifunctional displays, which have various functions in single-device systems without external circuits, are actively investigated as future human-machine interfaces owing to performability of unprecedented functions in compact design. However, their application is limited to visualize the mechanical/electrical signals in light. Herein, stretchable high-resolution multicolor synesthesia display, which can generate synchronized sound and light as input/output sources, is presented by transfer-printing. Transfer-printed emissive composite leads to display with enhanced optical performance and fine sound pressure level. Owing to inherent stretchability of the device, the synesthesia display can stably operate under static and dynamic deformation without distortion in sound relative to the input waveform. User-interactive synesthesia displays are demonstrated for visual -- acoustic encryption, which facilitate advanced encryption, as well as multiplex quick response code that bridges multiple domains with a single device. This approach provides new directions for multifunctional displays, with potential applications in reinforced authentication. [ABSTRACT FROM AUTHOR]

Chiroptical materials are gaining increasing interest due to their innovative character and their applications in optoelectronics and data encryption technologies. Fully harnessing the potential of building blocks from the "chiral pool", such as native cyclodextrins (CDs), as they often lack chromophores suitable for the construction of materials with significant chiroptical properties. Here, we present the synthesis and characterization of a two‐level molecular stack consisting of a point‐chiral element (CD) and an axially chiral element (biphenyl), capable of effectively translating the overall stereochemical information contained in CDs into stimuli‐responsive chiroptical properties. α‐ and β‐permethylated CDs were efficiently capped with two different 2,2'‐difunctionalized 1,1′‐biphenyl units. In CD derivatives containing the rigid 2,2′‐dihydroxy‐1,1′‐biphenyl cap, two intramolecular hydrogen bonds act synergistically as stereoselective actuators, enabling effective communication between the two levels and the transfer of nonchromophoric stereochemical information from the cyclic‐oligosaccharide to the atropoisomeric cap. The chiroptical properties can be finely tuned by external stimuli such as temperature and solvent. The way chirality is transferred from the CD platform to the biphenyl cap was revealed thanks to crystallographic and computational analyses, together with electronic circular dichroism (ECD) studies. [ABSTRACT FROM AUTHOR]

Optical image/data encryption techniques are mostly based on the manipulation of spatial distributions of light's amplitude, phase, and polarization. Information encoding with phase involves complex interferometric set-up and polarization encoding requires Stoke's parameter measurement. Hence, they create difficulties in optical implementation. Considering the practical limitations, in this study, we demonstrate a method of single-shot intensity recording-based color image encryption by encoding the information in binary polarization states. The proposed method does not require Stoke parameter calculation. As a proof-of-concept, we demonstrated the technique with coherent and partially coherent light sources. Use of partially coherent light overcomes the speckle problem and makes the system cost-effective, useful for practical applications. [ABSTRACT FROM AUTHOR]