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This research examines how a firm's cloud storage implementation affects different types of security breaches in both the short- and long-term. Building on the attention-based view, we find that cloud storage implementation positively relates to a firm's external breaches and accidental internal breaches in the short-term. However, the positive relationship between cloud storage implementation and external breaches diminishes over time and becomes insignificant long-term. Our results demonstrate a long-term security advantage of cloud storage in reducing accidental internal breaches. We did not find a significant association between cloud storage and malicious internal breaches. Findings highlight the need for firms to direct limited resources to different security risks in the short- and long-term of cloud storage implementation over time. This research contributes to our understanding of cloud storage's security implications and explicitly theorizes the role of attention in firm IT security management. We contribute to the attention-based view by contextualizing the theory to IT security. We highlight temporal dynamics through distinct attentional mechanisms, including selective attention, attentional flexibility, and attentional vigilance. [ABSTRACT FROM AUTHOR]

Summary: The exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security. [ABSTRACT FROM AUTHOR]

Data auditing permits data owners to confirm the reliability of their personal information without obtaining it from an unsafe cloud. Advertising cloud storage has widely adopted this characteristic. Data integrity is more difficult to maintain, and data through the cloud is not always secure. The ability to access data remotely through cloud storage makes it necessary to protect it against unauthorized access, hackers, and other malicious activity. In order to overcome these concerns, a hybrid Advanced Encryption Standard- Elliptic Curve Cryptography (AES-ECC) algorithm and Merkle Hash (MHT) Tree is developed. The user data is sent to the cloud service provider via blockchain for storage purposes. The key generation and encryption are carried out using a hybrid AES-ECC encryption technique, which is used to generate both private and public keys. The encrypted user file is then divided into several blocks. The tags for the split encrypted blocks are then generated using SHA256, which provides the hash value of each smaller file. Cloud Service Provider (CSP) constructs an MHT by employing generated hashtags to return the proof to user information after establishing hashtags for encrypted file blocks in the blockchain storage. Merkle hash tree is frequently employed to validate data integrity. Furthermore, the third-party auditor determines if the proofs generated by CSP match one from the user to check the tamper. The simulation analysis shows that the accuracy, encryption, decryption, key generation, and execution time reached for the proposed approach is 96%, 190 s, 124 s, 5.9 s, and 440 s. The proposed algorithm effectively validates data integrity and provides improved data security in blockchain environment. [ABSTRACT FROM AUTHOR]

With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red–black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure. [ABSTRACT FROM AUTHOR]

Users are more comfortable trusting their sensitive information to the cloud as its security continues to improve. However, when there are several encrypted files, each with its own set of keywords for indexing, the storage overhead grows exponentially, and search efficiency suffers. Therefore, this work provides a technique for searching encrypted cloud data that makes use of features to match joint keywords (FMJK). Joint keywords are generated by randomly selecting a subset of the data owner's non-duplicated keywords choice among the documents' extracted keywords; together, these keywords form a keyword dictionary. Every combined keyword matches with a document's feature as well as a query keyword, making the former's result considered a dimension of a document's index with the latter's result considered a dimension about the query trapdoor. Its BM25 method is then utilized for arranging the top k results by the inner product between the document index and the trapdoor. [ABSTRACT FROM AUTHOR]

With the escalation of multimedia data, cloud technology has played a very important role in its management with its promising computing and storage capabilities. Cloud computing leverages high scalability, resources, and on-demand services, which have a remarkable impact on assisting multimedia data storage. Meanwhile, any alteration in the outsourced multimedia data is a severe menace. Numerous researchers have focused on providing validation of integrity through the auditing mechanism. Regrettably, their mechanisms are focused on the traditional public-key cryptosystem and cannot withstand quantum attacks. Additionally, the centralised approach of completely relying on a third-party auditor (TPA) of existing mechanisms may lead to biased auditing results. Hence, employing the advantage of resistance against quantum attack and removing the liability of certificates with lattice cryptography and an identity-based cryptosystem, we have presented a new auditing framework by utilising blockchain technology. Blockchain technology is employed in order to track the activities of the cloud service provider and TPA, resulting less dependency on TPA. Security analysis portrays security of the framework under the random oracle model, which depends is based on the Shortest Integer Solution (SIS) problem of the lattices. Reduced computation, communication and storage overheads with efficient features and performance is observed for the proposed mechanism through analysis conducted with similar mechanisms. As a result, better operability is achieved by the proposed mechanism. [ABSTRACT FROM AUTHOR]

In this paper, a neural network guidance based on sample dimension reduction is proposed for a reusable launch vehicle. Different from previous works, the training samples of neural networks use Pearson correlation matrix analysis for dimension reduction, which significantly reduces the data volume of training samples and improves the efficiencies of sample construction, data storage, and neural network training. First, the analytical samples are generated by integrating dimensionless energy longitudinal motion equations. Then, the input parameters are processed by Pearson correlation matrix analysis, significantly reducing the data volume of training samples while ensuring the training effectiveness. Furthermore, the neural network is utilized to learn the mapping relationship between reduced-dimension flight states and predictive range-to-go, eliminating numerical integration calculations and improving the real-time performance of guidance. Finally, an extended Kalman filter (EKF) is implemented online for aerodynamic parameter identification, which dramatically enhances the adaptability to internal uncertainties and external disturbances. Compared with the traditional numerical guidance method, simulation results demonstrate that the proposed guidance exhibits superior real-time performance and faster computation speed while ensuring high accuracy and strong robustness to perturbations. [ABSTRACT FROM AUTHOR]

In the present time, cloud platforms are gaining more popularity for outsourcing features. So, many public or private organizations are fascinated towards cloud technology since it can handle enormous amounts of data. Cloud services also enable secure data exchanges among the registered and legitimate users. Depending on data sensitivity, cloud dependent data transfer suffers from security and privacy issues. This raises a big challenge for cloud dependent data sharing. Available solutions are having some limitations such as failure at single point, inconvenient and inefficient in terms of weak data model or revocation of user. In this research, we have suggested a quorum based scheme. In the suggested scheme, the quorum concept is assimilated with HM-ACS. For non-quorum and quorum approaches, experiments are performed. Thus, experimental analysis proved that the proposed schemes are performing much better in terms of key generation, memory required, encryption decryption and computation requirements. These schemes are also efficient and secured against all threats for data and user privacy. [ABSTRACT FROM AUTHOR]

In the era of big data in response to the conflict between the reliability enhancement of user data in cloud server storage and the strategy for removing duplicate data, a heterogeneous server data security deduplication method was proposed blockchain-based smart contracts. Leveraging the decentralized, tamper-proof, and publicly transparent characteristics of blockchain, as well as the automation capabilities of smart contracts, this method could achieve data storage security, reliability, and privacy protection. Specifically, the method combined secret sharing and blockchain smart contract technology to design a secure and efficient cloud storage data deduplication service. Moreover, by replacing the role of centralized third-party entities with blockchain and mitigating server heterogeneity through smart contract scripts, potential security risks were eliminated. Experimental results demonstrated that, under various scenarios with different file sizes and block quantities, the average computational overhead of this method was 65.42% to 115.77% lower than the comparative solutions, and the average storage overhead was 7.94% to 19.50% lower. Additionally, for varying numbers of heterogeneous storage servers, this method exhibited significantly lower average computational and storage overhead, with reductions of 67.27% to 177.89% and 34.01 % to 72.89%, respectively. Therefore, the proposed approach could outperform two existing blockchain-based deduplication method in terms of security, computational and storage efficiency. [ABSTRACT FROM AUTHOR]

Cloud computing and storage processing is a big service for maintaining a large number of data in centralized servers to store and retrieve data depending on use to pay as a service mode. Due to increasing storage depending on duplicate copy presence during different sceneries, the size increase leads to cost increase. To resolve this problem, efficient Deduplication techniques are implemented to reduce storage. Redundant Sparse Look-up Indexing (RSLI) based file Deduplication using Semantic Cluster Content Chunking (SCCC) for optimizing cloud storage. To optimize the cloud storage contiguous fragmentation was applied to reduce the storage blocks. Initially, preprocessing can be used to track content availability by checking file name, size, and type patterns using file space optimization techniques. Also, the file can be split into hash-indexed chunks by implementing predefined methods based on these. The content union can also be compared to other files through Sparse Lookup Content Source Duplication (SLCSD). Their use helps to identify the number of overlaps between files in different transformations. Distance Vector Weightage Correlation (DWWC) Document Similarity Weights Based on Presence Counts help to include and cluster related documents. Finally, the RCB compares the document based on the coefficient match case contents present in the cluster using the similarity weight to find the duplicate content. The simulated results demonstrated high performance in terms of precision, recall, storage efficiency, and time complexity when utilizing content file types. The proposed method enhances de-duplication performance by efficiently locating duplicate files, improving indexing, and reducing storage space in the cloud environment. The proposed method results are precision is 96.9%, recall is 97.8%, the false rate is 2.6% and storage redundant is 36.9% than other methods. [ABSTRACT FROM AUTHOR]

As with IoT devices, cloud computation increases its applicability to other areas that use the information and puts it in a commonplace that is necessary for computing and analysis. These devices need the cloud to store and retrieve data since they are unable to store and process data on their own. Cloud computing offers a variety of services to consumers, including IaaS, PaaS, and SaaS. The usage of cloud resources for data storage that may be accessible by all users associated with cloud computing is a key disadvantage of cloud computing. Without disclosing the data's contents, the usage of Public Key Encryptions with Keyword Search (PEKS) secures publicly encrypted keys against third-party search capabilities that are not trustworthy. PEKs provide a security risk every time Interior Keywords Guessing Attacks (IKGA) are discovered in the system since an unauthorized service estimates the keywords in the trapdoor. This issue can be resolved using various methodologies such as the Certificateless Fleshed Public Key Authenticated Encryption of Keyword Search (CL-HPAEKS), which also uses Altered Elliptic Curve Cryptography (MECC), and the Mutation Centred Flower Pollinations Algorithm (CM-FPA), which uses optimization in keys to be improving the algorithm's performance. The system's security is achieved by adding a Message Fragments 5 (MD5) scramble mechanism. The proposed system achieves system security of 96%, and it takes less time to implement than earlier encryption methods. [ABSTRACT FROM AUTHOR]

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

In order to improve the operational efficiency of traditional cloud storage data segmentation clustering methods, the author proposes a machine learning based cloud storage data segmentation clustering method. Reasonably extract multiple small datasets from the cloud storage database, which contain all natural clusters in the cloud storage database. Construct a similarity matrix based on the definition of similarity. Using nonlinear kernel principal component algorithm to measure the similarity of data in the similarity matrix, data with the same features are grouped together through similarity measurement, and a mixed Gaussian distribution probability density model is used to calculate the posterior probability of different categories of data, implement segmented clustering of cloud storage data by comparing probability sizes. The experimental results show that the proposed method can shorten the clustering running time, reduce the clustering variation to 29%, and effectively improve the smoothness of the clustering results. [ABSTRACT FROM AUTHOR]

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks. [ABSTRACT FROM AUTHOR]

A dataset consisting of numerically simulated oceanic velocities and sea surface height changes, provided conjointly from Eulerian and Lagrangian points of view, is made available as cloud-optimized archives on a cloud storage platform for unrestricted access. The Eulerian component of the dataset comprises oceanic velocity components at 0 m and 15 m depth, as well as total and steric sea surface height changes, obtained at hourly time steps for one year, with an approximate horizontal resolution of 1/25 degree on an irregular global geographical spatial grid, from the HYbrid Coordinate Ocean Model. The Lagrangian component of the dataset comprises the trajectories of particles advected in the Eulerian velocity field of the model. The particles were advected forward and backward for 30 days from a regular 1/4 degree grid in order to achieve 60-day long trajectories at 0 m and 15 m depths, with start times separated by 30 days, in 11 releases. This integrated dataset may help to link Eulerian and Lagrangian observational perspectives. [ABSTRACT FROM AUTHOR]

Big Data (BD) is a concept that deals with enormous amounts of data storage, processing, and analysis. With the exponential advancement in the evolution of cloud computing domains in healthcare (HC), the security and confidentiality of medical records have evolved into a primary consideration for HC services and applications. There needs to be more than the present-day cryptosystems to address these troubles. Therefore, this paper introduces a novel Three-Factor Authentication (3FA) and optimal Map-Reduce (MR) framework for secure BD transmission over the cloud with Secure Hashing Authentication XOR-ed Elliptical Curve Cryptography (SHAXECC). The authentication procedure is initially carried out with the SHA-512 algorithm, which protects the network from unauthorized access. Next, data deduplication is done using the SHA-512 algorithm to eliminate duplicate files. After that, an optimal MR design is introduced to handle a large amount of BD. In an optimal MR, the mapper uses the Modified Fuzzy C-means (MFCM) clustering approach to initially form the BD clusters. Then, the reducer uses the Levy Flight and Scoring Mutation-based Chimp Optimization Algorithm (LSCOA) to form final BD clusters. Finally, the SHAXECC is used to transmit the data securely. Experiments are performed to compare the superiority of the proposed technique with the existing techniques in terms of some performance measures. The proposed approach outperformed other existing models concerning clustering and security measures. So, the proposed model is the best for data protection and privacy in cloud-enabled HC data. [ABSTRACT FROM AUTHOR]

The enormous growth of data requires a large amount of storage space in the cloud server, which occupies mostly by the redundant data. The deduplication technique avoids redundancy to utilize cloud storage effectively. Chunking is the process to break the data into chunks, and determine duplicates. Many algorithms exist, to handle the deduplication efficiency, reducing the chunk variance, and improving the computational overhead continue to be a challenging task. To solve the above challenge, we propose a smart chunker (SC) algorithm, which operates with hybrid chunking based on file size as file-level and content-defined chunking (CDC). File-level chunking is assigned only for less than 2 KB file size, and the exceeding file size falls with CDC. We aim optimus prime chunking (OPC) in CDC to break the chunks with prime numbers and involves a minimum number of comparisons with fewer conditions and low computation overhead. This work reduces the processing time without hash and window for computation. Thus, it provides a constant average chunk size to distribute the equal chunk variance in the cloud storage with a 17% reduction in chunk time. Our OPC result attains high throughput of more than 3.3 × , compared to other CDC algorithms. [ABSTRACT FROM AUTHOR]

Due to the severe COVID-19 pandemic, securing and processing patient's health data for decision-making is challenging. The proposed study introduces an edge computing-assisted hybrid deep learning mechanism to mitigate this issue. Initially, the first layer in a proposed framework collects the input images and forwards them to the edge layer. Here, several edge devices are deployed, and COVID-19 disease detection is performed through a new hybrid attention capsule autoencoder (HA_CAE). Finally, the samples are securely stored in the cloud layer, in which the reliability of patient information is increased. The simulation analysis shows that the proposed model gains higher detection performance, like accuracy (99.1%), precision (97.6%), sensitivity (98.9%), specificity (96.8%), F1-score (98.2%), and MCC (97.6%). [ABSTRACT FROM AUTHOR]

Existing attribute-based proxy re-encryption schemes suffer from issues like complex access policies, large ciphertext storage space consumption, and an excessive authority of the authorization center, leading to weak security and controllability of data sharing in cloud storage. This study proposes a Weighted Attribute Authority Multi-Authority Proxy Re-Encryption (WAMA-PRE) scheme that introduces attribute weights to elevate the expression of access policies from binary to multi-valued, simplifying policies and reducing ciphertext storage space. Simultaneously, the multiple attribute authorities and the authorization center construct a joint key, reducing reliance on a single authorization center. The proposed distributed attribute authority network enhances the anti-attack capability of cloud storage. Experimental results show that introducing attribute weights can reduce ciphertext storage space by 50%, proxy re-encryption saves 63% time compared to repeated encryption, and the joint key construction time is only 1% of the benchmark scheme. Security analysis proves that WAMA-PRE achieves CPA security under the decisional q-parallel BDHE assumption in the random oracle model. This study provides an effective solution for secure data sharing in cloud storage. [ABSTRACT FROM AUTHOR]

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system. [ABSTRACT FROM AUTHOR]

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. [ABSTRACT FROM AUTHOR]

In the applications of cloud storage, ensuring data's integrity is an important research problem and kinds of remote integrity auditing protocols have been proposed. A classical auditing method is checking the validity of returned proof from cloud service provider, which is calculated by aggregating designated data blocks and their tags. In order to obtain lower storage redundancy and communication overhead, Yang et al. recently designed a compressive integrity auditing technique for secure cloud storage, in which only tags are needed to be stored on cloud. Inspired by Yang et al.'s compressive scheme, in this paper, we introduce a general construction of compressive integrity auditing protocol from strong homomorphic encryption scheme, which greatly enriches the implementations of compressive integrity auditing schemes. Moreover, our general construction can be twisted for some special applications. In particular, if the original data owner only wants to outsource its auditing process to designated verifier instead of any third party auditor, then our twisted construction becomes a framework of implementing compressive integrity auditing protocols with designated verifier. Finally, in order to show the power of our general construction, we give a concrete instantiation and compare its performance with two related works, which shows that our proposed scheme is very competitive and thus can be used in future applications of cloud storage. [ABSTRACT FROM AUTHOR]

In recent years, cloud storage services have risen widely, and how to ensure the integrity of outsourced data in cloud setting catches more and more attention. Kinds of integrity auditing protocols, additionally attaching tags on original data file, have been proposed. In order to compress the storage and communication overheads, Yang et al. presented a compressive auditing technique, which is identity-based and quantum computing resistant. Note that Yang et al. also claimed that their protocol is "suitable" for outsourcing auditing process to third party auditor (TPA). However, in this paper, we first pointed out that Yang et al.'s compressive protocol is essentially private auditing since the data owner needs to confidential transmit secret value to TPA. In fact, if TPA shares this value to cloud service provider (CSP), then Yang et al.'s scheme will become completely insecure. Then, we propose a new identity-based protocol, which is based on lattice and thus still secure in future quantum computing. Meanwhile, our proposed protocol belongs to public auditing instead of private auditing. Finally, we analyze the performance of our proposed protocol, which shows that it is competitive in the cloud storage applications. [ABSTRACT FROM AUTHOR]

Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has received much less attention to date. In this paper, we propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. They make up a pair. After going through a shared encoder and a shared head network, the consistency between the output representations are maximized with introducing two variants of contrastive losses to respectively facilitate downstream classification and segmentation. To demonstrate the efficacy of our method, we conduct experiments on three downstream tasks which are 3D object classification (on ModelNet40 and ModelNet10), shape part segmentation (on ShapeNet Part dataset) as well as scene segmentation (on S3DIS). Comprehensive results show that our unsupervised contrastive representation learning enables impressive outcomes in object classification and semantic segmentation. It generally outperforms current unsupervised methods, and even achieves comparable performance to supervised methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Retrieval of key mangrove traits based on Sentinel-2 surface reflectance data. • Gaussian Processes Regression (GPR) was trained on PROSAIL simulations and active learning intelligent sampling techniques. • GPR model gives insight in the most relevant bands and delivers per-pixel uncertainty interval. • Optimal GPR models were implemented in Google Earth Engine for end-user mapping over space and time. • Time series gap-filling via Whittaker smoothing enabled temporal decomposition to monitor mangrove trait dynamics. As mangrove forests are facing escalating threats from anthropogenic and natural stressors, Earth observation capabilities are providing an unprecedented view of these vital ecosystems. This study aimed to leverage the extensive archive and continuous stream of Sentinel-2 (S2) data and its integration into cloud-computing platforms to map key mangrove traits, indicative of ecosystem health and resilience. Specifically, a hybrid retrieval model implemented in Google Earth Engine (GEE) was operationalized for quantitative estimation of multiple mangrove traits from S2. For this purpose, the leaf-to-canopy radiative transfer model (RTM) PROSAIL in synergy with non-parametric Gaussian Processes Regression (GPR) were selected for hybrid retrieval models of effective Leaf Area Index (LAI e), Leaf Chlorophyll Content (C a b ), Leaf Water Thickness (C w), and Leaf Dry Matter (C m). GPR models were trained and optimized with a PROSAIL-based simulated dataset in combination with active learning sampling techniques to reduce the training dataset towards the most informative samples. Validation against in situ measurements showed high accuracy models achieved for LAI e ( R 2 = 0.693, NRMSE = 12.03 %) and Cab ( R 2 = 0.689, NRMSE = 13.29 %), while moderate performances were obtained for Cm ( R 2 = 0.597, NRMSE = 16.37 %) and Cw ( R 2 = 0.442, NRMSE = 16.66 %). The GPR models were subsequently implemented into GEE for processing of a time series data stream ranging from 2019 to 2023, together with the cloud-gap filling by applying a Whittaker smoother. Seamless maps of four key mangrove traits were generated simultaneously with per-pixel uncertainty estimation. The entire GEE-based workflow enables time series decomposition to uncover trends and seasonal dynamics. Supported by field validation, this study demonstrates a scalable approach for spatiotemporal monitoring of key mangrove traits using hybrid retrieval models via GEE implementation. These advances may contribute to national-scale monitoring in Vietnam and beyond to support mangrove ecosystem protection efforts and management thereof. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes. [ABSTRACT FROM AUTHOR]

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Summary: The Hadoop distributed file system (HDFS) effectively manages data by segmenting it into blocks distributed across DataNodes in its cluster. While default block sizes in Hadoop 2.x and 1.x are 128 and 64 MB, respectively, they can be customized for larger files. HDFS ensures data reliability by replicating blocks across multiple DataNodes, but this can introduce high latency in cloud storage during heavy network traffic, particularly in big data processing. To address this, we introduce swarm intelligence with a chaotic leader and a salp (SI‐CLS) optimization algorithm. This algorithm reduces network traffic between racks in HDFS by optimizing block distribution. The SI‐CLS algorithm calculates a fitness value for each block, aiming to increase data availability and reduce latency. Performance metrics, including latency, data availability, and load balancing, indicate the effectiveness of SI‐CLS. Adopting this algorithm enhances HDFS performance, ensuring better data availability and lower latency, resulting in improved system reliability. [ABSTRACT FROM AUTHOR]

Currently, there is a growing trend among users to store their data in the cloud. However, the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks. Additionally, when users perform file operations, the semantic integrity of the data can be compromised. Ensuring both data integrity and semantic correctness has become a critical issue that requires attention. We introduce a pioneering solution called Sec-Auditor, the first of its kind with the ability to verify data integrity and semantic correctness simultaneously, while maintaining a constant communication cost independent of the audited data volume. Sec-Auditor also supports public auditing, enabling anyone with access to public information to conduct data audits. This feature makes Sec-Auditor highly adaptable to open data environments, such as the cloud. In Sec-Auditor, users are assigned specific rules that are utilized to verify the accuracy of data semantic. Furthermore, users are given the flexibility to update their own rules as needed. We conduct in-depth analyses of the correctness and security of Sec-Auditor. We also compare several important security attributes with existing schemes, demonstrating the superior properties of Sec-Auditor. Evaluation results demonstrate that even for time-consuming file upload operations, our solution is more efficient than the comparison one. [ABSTRACT FROM AUTHOR]

Creating photonic crystals that can integrate and switch between multiple structural color images will greatly advance their utility in dynamic information transformation, high‐capacity storage, and advanced encryption, but has proven to be highly challenging. Here, it is reported that by programmably integrating newly developed 1D quasi‐periodic folding structures into a 3D photonic crystal, the generated photonic superstructure exhibits distinctive optical effects that combine independently manipulatable specular and anisotropic diffuse reflections within a versatile protein‐based platform, thus creating different optical channels for structural color imaging. The polymorphic transition of the protein format allows for the facile modulation of both folding patterns and photonic lattices and, therefore, the superstructure's spectral response within each channel. The capacity to manipulate the structural assembly of the superstructure enables the programmable encoding of multiple independent patterns into a single system, which can be decoded by the simple adjustment of lighting directions. The multifunctional utility of the photonic platform is demonstrated in information processing, showcasing its ability to achieve multimode transformation of information codes, multi‐code high‐capacity storage, and high‐level numerical information encryption. The present strategy opens new pathways for achieving multichannel transformable imaging, thereby facilitating the development of emerging information conversion, storage, and encryption media using photonic crystals. [ABSTRACT FROM AUTHOR]

The cloud storage service, known for its flexible and expandable nature, often has difficulties managing operating costs while ensuring dependable service and quick response times. This investigation presents a novel approach to optimizing cost efficiency in cloud storage systems by applying particle swarm optimization of the Markovian retrial queueing model in a generic setup by incorporating the working vacation and users' discouragement behavior. Some users may opt not to enter the system or join the retry pool to wait for their turn if the server is occupied. After returning from working vacation, if there is one user available for service, the server can interrupt the vacation period. The server is subject to breakdown and can be recovered after getting the repair. In the proposed model, the server is prone to catastrophes and can fail at any time, leading to the entire system breaking down, and no users being able to access it during this period. Chapman–Kolmogorov (CK) steady-state equations associated with the quasi-birth-death (QBD) process are constructed to make a mathematical design. The governing equations framed to derive the queue length distributions and various performance indices are solved using the recursive method and difference equation theory. The fuzzified parameters are used to develop the FM/FM/1/WV model, which is analyzed using a parametric nonlinear programming approach. To determine the optimal design parameters, the cost minimization problem has been done using the quasi-Newton method and particle swarm optimization. This model incorporates features such as server failures, retrials, and catastrophes, thereby reflecting the complex nature of cloud storage operations. A suitable illustration of cloud storage is taken for both classical and fuzzified models to facilitate the numerical results of performance indices and optimal decision descriptors. [ABSTRACT FROM AUTHOR]

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems. [ABSTRACT FROM AUTHOR]

In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks. [ABSTRACT FROM AUTHOR]

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field. [ABSTRACT FROM AUTHOR]

Summary: One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption. [ABSTRACT FROM AUTHOR]

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management. [ABSTRACT FROM AUTHOR]

Sparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block's size determined by considering architectural characteristics and accuracy requirements. Additionally, we dynamically assign precision to each block using a precision selection method that takes into account the value distribution of the original sparse matrix. We develop two distinct SpMV computation algorithms for BDMP: BDMP-PBP (Precision-based partitioning) and BDMP-TCKI (Tailored compression and kernel implementation). BDMP-PBP partitions the matrix into two independent matrices for separate computations based on block precision, offering flexibility for integration with other optimization techniques. Meanwhile, BDMP-TCKI focuses on achieving significant thread-level parallelism and memory utilization by tailoring an appropriate compressed storage format and kernel implementation for each block. We compare BDMP with NVIDIA's cuSPARSE library and three state-of-the-art SpMV methods, including SELLP, MergeBase, and BalanceCSR, using matrices from the University of Florida's SuiteSparse dataset collection. BDMP-PBP and BDMP-TCKI show average speedups up to 2.64 × and 2.91 × on Turing RTX 2080Ti, and up to 2.99 × and 3.22 × on Ampere A100. The results demonstrate that BDMP enables the optimization of computation speed without compromising the precision necessary for reliable results. [ABSTRACT FROM AUTHOR]

In this work, a language-level Semantics-Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of the point feature distribution, as well as the pseudo-feature generation, and a feature–geometry-based Mixup approach is further proposed to facilitate the distribution learning. Since a large number of point features could be generated from the learned distribution thanks to the semantics-conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic creates more significant opportunities to develop more Internet of Things-based systems. IoT has been helpful in remote monitoring and control of connected devices. This research aims to develop a Room Temperature Remote Monitoring System using an Arduino Board and Cloud storage. It provides remote room temperature monitoring, real-time reports and graphs, and a 3-way alert/alarm system when the observed temperature is out of range. The prototype developed is compared to the room thermometer and provides accurate results. [ABSTRACT FROM AUTHOR]

With the surge in cloud storage popularity, more individuals are choosing to store large amounts of data on remote cloud service providers (CSPs) to save local storage resources. However, users' primary worries revolve around maintaining data integrity and authenticity. Consequently, several cloud auditing methods have emerged to address these concerns. Many of these approaches rely on traditional public-key cryptography systems or are grounded in identity-based cryptography systems or certificateless cryptography systems. However, they are vulnerable to the increased costs linked with certificate management, key escrow, or the significant expenses of establishing a secure channel, respectively. To counter these limitations, Li et al. introduced a certificate-based cloud auditing protocol (LZ22), notable for its minimal tag generation overhead. Nonetheless, this protocol exhibits certain security vulnerabilities. In this paper, we devise a counterfeiting technique that allows the CSP to produce a counterfeit data block with an identical tag to the original one. Our counterfeiting method boasts a 100% success rate ∀ data block and operates with exceptional efficiency. The counterfeiting process for a single block of 10 kB, 50 kB, and 100 kB takes a maximum of 0.08 s, 0.51 s, and 1.04 s, respectively. By substituting the exponential component of homomorphic verifiable tags (HVTs) with non-public random elements, we formulate a secure certificate-based cloud auditing protocol. In comparison to the LZ22 protocol, the average tag generation overhead of our proposed protocol is reduced by 6.80%, 13.78%, and 8.66% for data sizes of 10 kB, 50 kB, and 100 kB, respectively. However, the auditing overhead of our proposed protocol shows an increase. The average overhead rises by 3.05%, 0.17%, and 0.45% over the LZ22 protocol's overhead for data sizes of 10 kB, 50 kB, and 100 kB, correspondingly. [ABSTRACT FROM AUTHOR]

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain. [ABSTRACT FROM AUTHOR]

As the processing power of mobile terminals increases, wireless network applications such as voice assistants can put more context-sensitive tasks on the mobile terminals, thus reducing the wireless network bandwidth needed and the cost of data storage in the cloud. Co-reference annotation, identifying the same semantics in context, is one of the critical techniques in these tasks. However, there are some problems with the existing co-reference annotation standards. First, the annotation is incomplete. Second, the types of annotated mentions are inconsistent. Third, there are currently no metrics for the above characteristics. Analyzing the above-mentioned issues, this paper proposes a new co-reference annotation standard. The new standard can annotate more semantics and co-reference relations and only adopts two types of mentions for annotation. Meanwhile, this paper presents a performance evaluation corpus and designs three performance metrics for evaluating the new standard according to the completeness of semantic annotation, the completeness of co-reference annotation, and the consistency of mention. The experiment shows that the new standard outperforms all the baseline methods and achieves 0.95 in the completeness of semantic annotation, 0.68 in the completeness of co-reference annotation, and 0.57 in the consistency of types of mentions. [ABSTRACT FROM AUTHOR]

Multi-user searchable encryption (MUSE) schemes provide solutions for searching over ciphertexts shared by different users, but the significant overhead of communication and computation leads to high latency in the group sharing system. Moreover, most MUSE schemes support only single-keyword search, and are vulnerable to the internal keyword guessing attack (IKGA). In this paper, to address these challenging tasks, we first propose a dual-server Public-key Puncturable Encryption with Conjunctive Keyword Search (dPPECKS) scheme in a group sharing scenario by extending the Punctrable Encryption (PE) and conjunctive keyword search (CKS) technologies. Our scheme realizes the efficient and dynamic update of team members, including user joining and exiting. Through rigorous analysis, we prove that our scheme can achieve IND-CKA2 security and resist IKGA. Extensive evaluation results demonstrate that our proposed scheme achieves significant improvements in terms of communication and computation costs. For example, compared with prior related work, with 90 people left in a team of 100, the storage cost of ciphertext is reduced from 80 to 1.8 KB. Under the same setting, it costs only 0.177 s to generate the ciphertext in our scheme, which is 90 times less than that in another scheme. [ABSTRACT FROM AUTHOR]

Using a novel hybrid-fusion neural network technique and cloud computing environment, this paper examines the relationship between individual flexibility, workplace interpersonal problems, and construction worker safety. The population is made up of construction firms based in Mashhad, Iran. This is an applied study, and the data collection approach used is a descriptive survey. The data was obtained using cloud-based storage, data analysis techniques, and data processing tools. The primary objective is to investigate the critical nature of personal safety on construction sites and techniques for minimizing construction accidents. The statistical population consisted of Mashhad, Iran-based construction firms. Due to the impossibility of studying the entire research population, a sample of n = 200 participants was chosen for this study. Following questionnaire distribution, 151 valid questionnaires were gathered. The primary study instrument was a 28-item questionnaire devised by the researcher based on the Questionnaire on the Experience and Evaluation of Work and occupational health. On a Likert scale, the things were rated. Cronbach's alpha coefficient was also used to determine the questionnaire's validity. SPSS-26 software was used to analyze the data in a MATLAB environment. The findings indicate that interpersonal conflict, workplace stress, supervisor involvement, physical injuries, dangerous incidents, and coworker participation are all connected with worker safety at a significant level of p 0.05. One of the research's advances is the use of new hybrid-fusion neural networks. Cloud computing has performed admirably in this study, taking into account the massive volume of data for speedy analysis and the development of a mechanism as an appropriate application. Additionally, the study was motivated by a dearth of comprehensive research on individual flexibility and interpersonal conflicts among construction workers. [ABSTRACT FROM AUTHOR]

Nowadays, digital medical images have become an essential source for the grand success of e-health technology. At the same time, the massive storage also plays a vital role. One of cloud storage's main objectives is the affordable and easily accessible storage of vast amounts of multi-structured data. The cloud paradigm gives an illusion of infinite storage of data. The future of Cyber-Physical Systems (CPS) relies upon technologies like cloud computing to thrive. However, the major lacuna is data security. This paper deals with the Confidentiality Integrity Availability (CIA) aspects required for cloud-based medical image repositories. Since it is for the medical image, the Region of Interest (RoI) is separated, and the integrity check is applied for RoI. A two-tier security for the medical image has been proposed, including an additional security layer for RoI. A 3-D Lorenz chaotic attractor has been used to generate the key where the keyspace is widely increased. Deoxyribonucleic Acid (DNA) based image diffusion in different stages of cryptosystem offered an average entropy of 7.98042 and a correlation of 0.002864 for RoI only and for ciphered medical image an average entropy of 7.99724 and a correlation of − 0.00063. Text encryption is performed over metadata to ensure the privacy of client authentication. Encrypted metadata and 320 bits have been generated for the RoI part embedded in an image's Non-Region of Interest (NRoI) part in the random pixel indexes obtained using a 1D Tent map. This proposed approach also gives a Graphical User Interface developed using Python 3.8 to support non-technical persons or medical practitioners. The proposed security framework provides a complete CIA triad for medical image repositories in the cloud. [ABSTRACT FROM AUTHOR]

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. Clinical relevance statement: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. Key points: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience. [ABSTRACT FROM AUTHOR]

To ensure both data security and usability in cloud environments simultaneously, Searchable Encryption (SE) emerges as a crucial technique. Through the utilization of Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy AttributeBased Keyword Search (CP-ABKS) scheme accomplishes keyword-based retrieval and fine-grained access control concurrently. However, existing CP-ABKS schemes with a single attribute authority entail costly user certificate verification and secret key distribution, leading to a performance bottleneck in distributed cloud systems. Thus, this study introduces a secure Multi-authority CPABKS (MABKS) system to overcome these challenges and alleviate the computation and storage burden on resource-limited devices within cloud systems. Furthermore, the MABKS system extends its functionality to support malicious attribute authority tracing and attribute updates. [ABSTRACT FROM AUTHOR]