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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]

EHRs sharing systems provide a secure and efficient way for patients and doctors to share information in smart healthcare. Due to the concern about data confidentiality and authorized access, the exploitation of attribute-based encryption (ABE) is widely adopted for EHRs sharing in cloud storage. However, most ABE-based EHRs sharing system only considered unilateral access control or supported non-flexible bilateral access control. Hence, we propose a lightwight and flexible healthcare data sharing system, LiVeRe, which enables fine-grained bilateral access control and moreover supports efficient user revocation and ciphertext integrity verification for the access control property. Technically, we employ the dual-policy framework to specify the access policy and pre-decryption techniques to alleviate the computational burden, and efficient revocation of user access rights by the KUNode algorithm. We also provide formal security models and correspondingly prove its security. Moreover, we conduct experiments on the cloud to demonstrate the practicality of our LiVeRe scheme. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of cloud storage, deduplication technology has been widely used. In personal cloud storage, files are diverse. Diverse files contain files of different sizes and different popularity, some of which have many copies. Existing deduplication schemes mainly focus on data security but fail to improve the overall performance, including low computing overhead for diverse data. Using deterministic tags based on convergent encryption to identify data may leak data information, while using fully random tags generated by complex encryption algorithms may generate more computing overhead. To address the above issues, we proposed an encrypted deduplication scheme based on files diversity (FD-Dedup). Diverse files are identified by semi-random tags. We also designed a semi-random tag generation (SRTG) algorithm that coordinates the computing overhead and security. Security analysis and performance comparison show that FD-Dedup can balance security and computing overhead for diverse files in personal cloud storage. [ABSTRACT FROM AUTHOR]

Copyright of China Medical Education Technology is the property of China Medical Education Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud storage is one of the most crucial components of cloud computing because it makes it simpler for users to share and manage their data on the cloud with authorized users. Secure deduplication has attracted much attention in cloud storage because it may remove redundancy from encrypted data to save storage space and communication overhead. Many current safe deduplication systems usually focus on accomplishing the following characteristics regarding security and privacy: Access control, tag consistency, data privacy and defence against various attacks. But as far as we know, none can simultaneously fulfil all four conditions. In this research, we offer a safe deduplication method that is effective and provides user-defined access control to address this flaw. Because it only allows the cloud service provider to grant data access on behalf of data owners, our proposed solution (Request-response-based Elliptic Curve Cryptography) may effectively delete duplicates without compromising the security and privacy of cloud users. A thorough security investigation reveals that our approved safe deduplication solution successfully thwarts brute-force attacks while dependably maintaining tag consistency and data confidentiality. Comprehensive simulations show that our solution surpasses the evaluation in computing, communication, storage overheads, and deduplication efficiency. [ABSTRACT FROM AUTHOR]

Access control of data that are outsourced to cloud storage is a challenging problem because data owners lose direct control over outsourced data. Attribute‐based encryption (ABE) is a potential cryptographic solution to provide confidentiality and flexible sharing of these outsourced data. However, the traditional ABE schemes do not meet the need of the current dynamic environment where data access not only considers the user's static and inherent attributes but also takes the user's contextual information such as location and time of access. This paper presents an improved ABE scheme using blockchain technology that can handle the frequently changing location and time attributes of data users efficiently, leading to support for fine‐grained access control of cloud storage embedding contextual information in the access policy of ABE. A prototype implementation of the proposed ABE scheme using solidity on the Ethereum platform and the experimental evaluation in terms of performance and execution cost shows a promising result. [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]

The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data. [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]

Cloud computing is very popular due to its unique features, such as scalability, flexibility, on-demand service, and security. A competent task scheduler is necessary to boost the efficiency of a cloud system, which executes several tasks at once. With the incorporation of cloud computing, the Internet of Things (IoT) has seen tremendous improvements recently. IoT devices produce various data sets they want to store in particular places on the cloud. Data and resources may be spread across several locations and accessible from the VM through suitable technology. Cloud computing has changed how resources are used, stored, and shared for industrial applications, including data, services, and applications. Virtual Machine Placement (VMP)'s main goal is to map Virtual Machines (VMs) to Physical Machines (PMs), allowing the VMs to be used to their fullest potential without interfering with the PMs that are currently running. VM performs the selected task and stores the data in a cloud location. Recently, many algorithms have worked on only VM selection or VM migration; instead of this, we also work on the data storage related to a particular IoT device in the cloud. It considerably lowers energy usage and offers a list of live VM migrations that must be completed to obtain the best solution and store the data on the cloud in a linked location. The authors present a novel scheduling technique that outperforms other widely recognized scheduling algorithms regarding load balancing in compression, specifically the quality of service parameters. In this research, we propose a Background-based task scheduling (BBTS) algorithm to decrease energy usage and boost throughput while choosing a light VM for any activity. Furthermore, the proposed approach is compared to various task scheduling methodologies, considering multiple performance metrics such as makespan time, resource utilization, success rate, and computation time. The evaluation is conducted on a task set ranging from 100 to 1000, with makespan time ranging from 55 to 654, resource utilization ranging from 35.45 to 42.13, success rate ranging from 88.63 to 96.48, and computation time ranging from 39.12 to 529.46. These metrics are compared to the corresponding algorithms (IWC, WOA, and ALO) utilized in this research study. [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 recent years, people are drastically using the internet. Their daily lives have grown to depend on it. and it now' connects to their social lives. In addition, internet usage has increased as a result of the COVID pandemic. Data growth and the associated need for storage space have emerged as major issues iIi cloud storage management. To improve the efficiency of cloud storage and save bandwidth of network data transmission, the duplication of cloud data has become a research goal, especially in the field of encrypted cloud data storage. Deduplication is a technique for removing data redundancy that was created to reduce redundant data in cloud storage and conserve system storage resources. Nowadays. due to the growth of cloud computing technologies. duplication techniques are increasingly used in cloud service centers. Therefore. many researchers have adopted several deduplication methods to remove redundant data from cloud storage. For secure deduplication, previous works have typically used third-party auditors to verify data integrity, but it may be suffered from data leaks by third-party auditors. Conventional methods also could not face more difficulties in the deduplication of big data to properly consider the two conflicting goals of high deduplication ratio and deduplication efficiency. In this paper, an improved blockchainbased secure data deduplication is presented with efficient cryptographic methods to save cloud storage securely. IIi the proposed method, an Attribute-based Role Key Generation (ARKG) method is implemented in a hierarchical tree manner to generate a role key when the data owners upload their data to the cloud service provider (CSP) and to allow authorized users to download the data, In the storage system. the smart agreement (agreement between the data owner and CSP) is done using SHA-256 (Secure Hash Algorithm-256) to generate a tamper-proofing ledger for data integrity, in which data is protected from illegal modifications and duplication detection is executed through hash-tag that can be formed by SHA-256. Content Sealed Encryption (CSE) is employed to encrypt data for data uploading by the data owners to the CSR The experimental results show that the proposed secure deduplication scheme is efficient according to data deduplication. storage costs, computational costs, and computation delay and it can give higher throughput Lind a low duplicate elimination ratio. [ABSTRACT FROM AUTHOR]

With the rapid growth of wireless sensor networks, secure data transmission, storage, and distribution in such networks has become an urgent demand. To defend against security risks such as data leakage, key compromise, and unauthorized misuse of data simultaneously, we propose a novel obfuscatable ciphertext-policy attribute-based re-encryption scheme with a specially designed obfuscator. The proposed scheme leverages program obfuscation to transform the re-encryption program codes into an unintelligible form and embed the private keys into the obfuscated implementation. Consequently, the proposed scheme protects data confidentiality and keeps the secrecy of the private key while providing fine-grained access control. Formal proofs for the security of the proposed re-encryption scheme and the obfuscator are provided. Extensive experiments have been conducted on representative platforms, including cloud servers, workstations, and embedded devices, to evaluate the computational efficiency and energy consumption of the scheme. Experimental results indicate that the scheme achieves high efficiency on various platforms and economical energy consumption on typical embedded devices with constrained resources. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]