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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]

Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things applications. In order to meet the users' demands and provide a cost-effective solution, cloud service providers offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%. [ABSTRACT FROM AUTHOR]

Storage savings and data confidentiality are two primary goals for outsourced storage. However, encryption by design destroys the content redundancy within plaintext data, so there exist design tensions when combining encryption with data reduction techniques (i.e., deduplication, delta compression, and local compression). We present EDRStore, an outsourced storage system that realizes encrypted data reduction to achieve both storage savings and data confidentiality. EDRStore's core idea is a careful design of the encryption and data reduction workflows. It proposes new key generation and encryption schemes to preserve the content similarity of encrypted data for deduplication and delta compression. It further proposes selective local compression based on content similarity, so as to achieve storage savings of encrypted data from both delta compression and local compression. Evaluation on real-world datasets shows that EDRStore achieves higher storage savings than existing encrypted storage approaches and incurs moderate performance overhead compared with plaintext storage. [ABSTRACT FROM AUTHOR]

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) and cloud storage play pivotal roles in supporting diagnosis, treatment processes, and healthcare. However, they also present significant threats to patient privacy and security. Safeguarding patient privacy has become an urgent practical concern. This paper introduces a privacy-preserving technique based on reversible medical data hiding in plaintext encrypted images using a neural network. Our approach starts by constructing a unified neural network model that integrates U-Net and AlexNet for precise classification and segmentation tasks. Subsequently, we employ plaintext encryption to conceal the region of interest (ROI), preventing visual disclosure of patient privacy while avoiding unwarranted attention from ciphertext encryption. Through a skipping hiding method, we encrypt and embed privacy information into the region of non-interest (RONI). Notably, the proposed scheme is separable, enhancing flexibility and security. For content owners, a joint neural network model divides the medical image into ROI and RONI, with ROI encrypted using an improved plaintext encryption method. Data hiders embed secret bits into RONI based on skipping hiding without accessing any original image information. Receivers can obtain desired information based on specific authority. Importantly, our scheme effectively addresses the challenge of separately storing medical images and diagnostic information, thereby reducing the risk of loss or mismatch. It mitigates the risk of privacy disclosure in diagnostic information without compromising ROI image quality. Experimental results demonstrate the superior accuracy of our scheme (accuracy, precision, recall, and F1-score all exceeded 0.98, with a remarkable recall rate of 0.9922), along with enhanced visual quality, robust plaintext encryption (plaintext-encrypted stego-image: average PSNR: 43dB, SSIM: 0.99, NC: 0.99), high efficiency and low complexity (overall processing time of 3.19 s) and complete reversibility (restored image: PSNR: +∞dB, SSIM: 1, NC: 1). Additionally, our scheme proves effective in addressing current privacy protection challenges. [ABSTRACT FROM AUTHOR]

Artificial intelligence embedded cloud computing technology can serve as a reliable storage for digital records in the fifth industrial revolution in South Africa. Some cloud storage facilities are embedded in the artificial intelligence powered electronic records management to ensure that digital archives and records are effectively managed and can be accessed anytime and anywhere as long as there is access to the Internet. Digital archives refer to the digitalised archival records and born digital archives that can be managed through artificial intelligence cloud computing technology. Artificial intelligence embedded cloud computing technology enables organisations to access and store information without managing their own physical devices or information technology infrastructure. Artificial intelligence refers to the use of intelligent robotic machines and AI powered software and applications to manage digital archives through artificial intelligence powered cloud storage facilities. Cloud computing as technology means using Internet servers for storage, management, and processing of digital archives and records for the entire life cycle. This is a qualitative study, where content analysis was used as a research methodology. Themes such as artificial intelligence, cloud computing technology and digital archives were used to search for literature. The findings indicate that artificial intelligence embedded cloud computing technology can store digital archives and records effectively and they can be protected through encrypted passwords and security codes for security purposes. The digital archives can also be accessed anytime and anywhere through the use of smart computer technology. The study recommends a framework on how artificial intelligence embedded cloud computing technology can be used to manage the digital archives and ensure easy access to such digital archives in South Africa. It is hoped that organisations in South Africa can adopt the framework to utilise artificial intelligence cloud computing technology to manage their digital archives effectively and efficiently. [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]

The purpose of this research is to assess the effectiveness of using cloud storage services as a means to facilitate the rehearsal process in online choral vocal education. Third-year students from three Chinese educational institutions participated in this study. Thus, this research involved a total number of 336 people (320 students and 16 teachers). Accordingly, students were divided into two groups based on the training approach. The first one, Group A, consisted of students who received choral vocal training under the conventional program without the application of online platforms and cloud storage services. The second one, Group B, included students who received education using technology-enabled training methods. They participated in choral rehearsals on Zoom using multimedia materials from Google Drive. According to the results of the final assessment, Group B performed better than Group A; the difference in the average performance score was 5% between the two groups. [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.)

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study's potential to impact accounting systems and financial reporting research and practice emphasizes its importance. [ABSTRACT FROM AUTHOR]

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]

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations' data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in "digital sustainability" and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology. [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]

The article discusses the successful establishment of a local rare blood registry in Brazil, focusing on the efforts of HEMOCE, a public blood center. By implementing a strategic screening panel and engaging donors, HEMOCE has identified over 500 rare donors since 2013, contributing significantly to the Brazilian Rare Blood Donors registry. The program's success is attributed to cost-effective screening methods, donor education, and a dedicated donor search team, making it a valuable model for other centers aiming to develop or improve their rare blood donor registries. [Extracted from the article]

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data's authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model. [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]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 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]

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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]

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]

With the growing global emphasis on renewable energy, the issue of renewable energy consumption has emerged as a hot topic of current research. In response to the volatility and uncertainty in the process of renewable energy consumption, this study proposes a renewable energy consumption strategy based on scenario reduction and flexible resource utilization. This strategy aims to achieve the efficient utilization of renewable energy sources through optimized resource allocation while ensuring the stable operation of the power system. Firstly, this study employs scenario analysis methods to model the volatility and uncertainty of renewable energy generation. By applying scenario reduction techniques, typical scenarios are selected to reduce the complexity of the problem, providing a foundation for the construction of the optimization model. At the same time, by fully considering the widely available small-capacity energy storage units within the system, a flexible cloud energy storage scheduling model is constructed to assist in renewable energy consumption. Finally, the validity and feasibility of the proposed method are demonstrated through case studies. Through analysis, the proposed scenario generation method achieved a maximum value of 26.28 for the indicator I D B I and a minimum value of 1.59 for the indicator I C H I . Based on this foundation, the cloud energy storage model can fully absorb renewable energy, reducing the net load peak-to-trough difference to 1759 kW, a decrease of 809 kW compared with the traditional model. [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]

Cloud storage has attracted widespread attention due to its advantages such as convenience, low cost, and easy scalability. With the explosion of cloud data volumes, security and performance issues are becoming major concerns. Countermeasures like data deduplication and integrity auditing techniques have been extensively studied. However, traditional auditable deduplication protocols face significant challenges related to confidentiality, reliability, and efficiency. Aiming to address these challenges, a blockchain-based auditable deduplication scheme for multi-cloud storage is proposed in this paper. Specifically, this scheme leverages blockchain technology and bilinear pairing cryptosystem to achieve an auditable deduplication mechanism that not only saves storage space but also checks data integrity. Furthermore, convergent encryption and threshold secret sharing algorithms are utilized to enhance storage confidentiality and system reliability. Batch auditing and anti-repeated auditing techniques are also adopted to improve auditing efficiency. Finally, security analysis and performance evaluation reveal that the proposed scheme achieves the expected security and performance requirements. [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]

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]

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps. [ABSTRACT FROM AUTHOR]

Remote data integrity auditing (RDIA) schemes can inspect the integrity of outsourced data. And deduplication improves the utilization of cloud storage space. Therefore, a considerable number of remote data integrity auditing schemes with deduplication mechanism (RDIA-DE) have been proposed by researchers. However, most of these schemes suffer from the following shortcomings. Initially, these schemes rely on honest and trustworthy third-party auditors (TPAs), which were difficult to achieve in reality. Additionally, the computational overhead of the proof-of-ownership (PoW) algorithms increases linearly with the number of challenged data blocks. In this paper, we put forward a novel RDIA-DE scheme based on blockchain. This scheme utilizes smart contracts to conduct data integrity audits, thereby eliminating the dependency on TPA and penalizing malicious cloud storage providers. Subsequently, we provided security proofs under the random oracle model (ROM). Ultimately, we conducted a performance analysis on six RDIA-DE schemes. The results demonstrate that the new scheme enjoys significant efficiency advantages. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become a pervasive phenomenon, with applications in a multitude of sectors, including healthcare, smart agriculture, smart cities, transportation, and water management. This has led to a significant generation of Big Data. In order to process this substantial volume of data efficiently, there is a pressing need for a platform capable of handling large quantities. However, real-time applications face challenges in cloud processing due to high latency. As a complementary infrastructure to the cloud, fog computing emerges as a viable solution by facilitating task processing, networking, and data storage in cloud data centers accessible to mobile users. The offloading of tasks represents a promising solution to the resource constraints inherent in IoT applications, particularly within the context of fog computing. This process entails the execution of particular components of mobile applications within a fog–cloud environment, to reduce execution time and energy consumption. The objective of our research is to optimize task offloading in IoT within heterogeneous environments, taking into account conflicting constraints. This optimization challenge is formulated as a multi-objective problem, with a particular focus on energy consumption and latency, as well as quality of service metrics. The proposed solution, TOF-NSGAII, is designed to respect the finite resources of fog computing, balancing workloads to meet the latency requirements of IoT tasks. The widely employed meta-heuristic, the non-dominated sorting genetic algorithm (NSGA-II), has been adapted to generate a set of non-dominated multi-objective task offloading optimization solutions, considering both energy consumption and latency. The experimental results demonstrate the efficacy of TOF-NSGAII in generating task offloading solutions that distribute executed tasks between fog and cloud computing environments in a judicious manner, based on their specific requirements. Furthermore, the generated non-dominated solutions demonstrate optimality in terms of energy consumption, with an average reduction of 12.18% compared to alternative approaches. It is noteworthy that our approach introduces only a marginal increase in latency, amounting to 0.38%, which can be considered negligible. [ABSTRACT FROM AUTHOR]

In public spaces, individuals encounter challenges due to the prevalence of malicious activities like theft and kidnapping. As the internet of things (IoT) and digital technology continue to expand rapidly, efforts to create safe environments are becoming increasingly sophisticated. To address these security concerns, a proposed solution involves the utilization of videocapturing technology with the help of a Raspberry Pi web camera. Videos of the surroundings are recorded, a digital signature algorithm is applied to protect the videos, and they are then transmitted to authorized individuals who use them for forensic analysis. This process allows for the identification and investigation of any suspicious or criminal activities. The captured video data is compared with a standard dataset using a deep learning process. By analyzing the content of the videos and identifying the potential threat objects, we can allow for prompt intervention or further investigation by relevant authorities. [ABSTRACT FROM AUTHOR]

In order to recover user’s queries in conjunctive keyword searchable encryption schemes, two attacks against the conjunctive keyword searchable encryption schemes were proposed, such as cross leakage attack and frequency matching attack. Firstly, a set of candidate keywords were extracted from the leakage. Then the keywords were filtered according to the keyword pair result pattern leakage and query frequency information respectively. Results show that in the cross leakage attack, with knowledge of only 10% of the dataset, the accuracy of query recovery can reach up to 90% if the keyword space is 100. And more than 50% of the queries are still able to recovered by the attacker if the keyword space is expanded to 1 000. In frequency matching attack, even with only inaccurate frequency distribution information known to the attacker, at least 70% of queries can be accurately recovered.

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]