Secure Cloud Storage Research Articles

Access management based on deep reinforcement learning for effective cloud storage security

Access management based on deep reinforcement learning for effective cloud storage security

Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discussion on challenges, future research directions, and the need for optimization in real-time applications.

Hybrid diffusion-based visual image encryption for secure cloud storage

Hybrid diffusion-based visual image encryption for secure cloud storage

Understanding Leakage in Searchable Encryption: a Quantitative Approach

Searchable encryption, or more generally, structured encryption, permits search over encrypted data. It is an important cryptographic tool for securing cloud storage. The standard security notion for structured encryption mandates that a protocol leaks nothing about the data or queries, except for some allowed leakage, defined by the leakage function. This is due to the fact that some leakage is unavoidable for efficient schemes.\\ Unfortunately, it was shown by numerous works that even innocuous-looking leakage can often be exploited by attackers to undermine users' privacy and recover their queries and/or data, despite the structured encryption schemes being provably secure. Nevertheless, the standard security remains the go-to notion used to show the 'security' of structured encryption schemes. While it is not likely that researchers will design practical structured encryption schemes with no leakage, it is not satisfactory that very few works study ways to assess leakage.This work proposes a novel framework to quantify leakage. Our methodology is inspired by the quantitative information flow, and we call our method q-leakage analysis. We show how $q$-leakage analysis is related to the standard security. We also demonstrate the usefulness of q-leakage analysis by analyzing the security of two existing schemes with complex leakage functions.

Enhancing cloud storage security through blockchain-integrated access control and optimized cryptographic techniques

Enhancing cloud storage security through blockchain-integrated access control and optimized cryptographic techniques

Neural secret key enabled secure cloud storage with efficient packet checker algorithm

Cloud computing technology is utilized for the commercial purpose of implementing virtualization for the storage of data, which is then controlled and made accessible to users via remote servers. With the increased use of various communication devices and direct access to web servers, there is a favorable opportunity to orchestrate attacks and exploit vulnerabilities in a system. Distributed Denial of Service (DDoS) is a commonly used technique employed by attackers in cloud environments. This study introduces a security layer incorporating a Packet Checker Algorithm (PCA) to detect and eliminate counterfeit packets. The algorithm takes into account transmission delay time as well as minimum and maximum thresholds, thereby enhancing the response time of the Intrusion Detection Process (IDP) in a cloud environment that utilizes neural encryption. Neural encryption is used to synchronize neural networks and create new public channel protocols for a secure cloud storage paradigm. This study greatly expands upon the hop count inspection and filtering method. It incorporates the time slot filtering function and implements a unique key set to differentiate between genuine packets and falsified packets. This novel methodology has the capability to detect Distributed Denial of Service (DDoS) attacks and related anomalies during the initial phases of data transmission. The technique considers Time-to-Live (TTL), Hope count, and Transmission-Delay-Time as crucial components.

Data-Driven Framework for Cloud Storage Security Optimization: Leveraging Predictive Analytics and Machine Learning to Enhance Threat Detection and Incident Response

As cloud storage adoption accelerates, securing sensitive data against evolving threats, including Advanced Persistent Threats (APTs), Zero-Day Exploits, and Insider Threats, has become paramount. This research introduces a data-driven framework that harnesses predictive analytics, machine learning (ML), and deep learning (DL) techniques to fortify threat detection and incident response in cloud storage environments. By integrating real-time monitoring via Security Information and Event Management (SIEM) systems, anomaly detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and predictive modeling through Graph-Based Anomaly Detection (GBAD), our framework identifies potential security risks and optimizes countermeasures. Leveraging ML algorithms, such as Random Forest and Support Vector Machines (SVM), our approach analyzes historical incident data, user behavior, and system logs to predict and prevent attacks. Key benefits include proactive security measures, reduced response times via Security Orchestration, Automation, and Response (SOAR), and minimized data breaches through Containerization (Docker) and Serverless Computing (AWS Lambda). This research advances the development of intelligent cloud storage security solutions, ensuring robust protection for sensitive data in cloud-based infrastructure, compliant with PCI-DSS, HIPAA, GDPR, NIST Cybersecurity Framework, and ISO 27001.

Thumbnail-Preserving Encryption Technology Based on Digital Processing

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

Convergent encryption enabled secure data deduplication algorithm for cloud environment

SummaryThe 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.

QSKCG: Quantum‐based secure key communication and key generation scheme for outsourced data in cloud

AbstractIn the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage.

IoT data encryption and phrase search-based efficient processing using a Fully Homomorphic-based SE (FHSE) scheme

In this study, the Efficient Multikeyword Fully Homomorphic Search Encryption (EMK-FHSE) model is proposed to improve cloud storage security for sensitive data. When fully homomorphic encryption (FHE) and search encryption (SE) technologies are coupled, Fully Homomorphic Search Encryption (FHSE) is a strategy that realizes the shared information's controlled privacy and search security. As more and more encrypted data is kept on cloud servers (CSs), a single-keyword SE approach may cause multiple keyword index duplication concerns, making it challenging for CSs to search for the encrypted information. To reduce these problems, a novel efficiency bottleneck has been developed. An Adaptive Privacy-Preserving Fuzzy Multi-Keyword Search (APPFMK) approach is presented to address the difficulties of low search effectiveness in a single-keyword searching strategy and the high processing cost of the existing multi-keyword schemes. Cloud servers (CS) hold enormous volumes of encrypted data, and the necessary encrypted index is transmitted to the closest edge node (EN) to enable multi-keyword searches and supported decryption. According to security research, the EMK-FHSE multi-keyword index is safe in distinguishability under chosen keyword attacks. The results section compares the proposed model's search, storage, trapdoor, calculation, storage and validation times to those of several other models. The proposed model could achieve the following values: 60.81 kb for storage, 10.92 for the trapdoor, 6.85 ms for search, 0.44 ms for computation cost by changing the keyword in a trapdoor, 156.31 ms for computation cost by changing the keyword in a dictionary, 0.44 kb for storage cost by changing the keyword in a trapdoor, 1.81 kb for storage cost by changing the keyword in a dictionary and 0.016seconds for verification time, respectively.

SECURE CLOUD STORAGE PROTOCOL WITH DATA DYNAMICS USING DSCS PROTOCOL

The goal of the project "Secure Cloud Storage Protocol With Data Dynamics Using DSCS Protocol" is to create a cutting-edge cloud storage protocol that cleverly integrates network coding technologies to combine efficiency and security. The goal of this project is to provide strong protection against data loss and illegal access while improving the performance and dependability of cloud storage systems. The suggested protocol distributes and encrypts data among several cloud network nodes using network coding techniques. This strengthens the system against possible data loss or corruption by introducing redundancy and error correction in addition to optimizing data transit and storage efficiency. Key Words: cloud, private key, sensitive data, Network Coding

Secure Cloud Storage Solution with “Seafile” & “NextCloud”: A Resilient Efficiency Assessment

Storage and file synchronization have become increasingly important services in our everyday lives. With cloud storage providers giving users access to file space in the cloud, one might wonder how efficient these cloud storage providers are with their encryption methods. There are many different cloud storage providers and programs to set up a cloud storage solution. This paper focuses on two secure cloud storage solutions, Seafile, and NextCloud, and determines the efficiency of these cloud storage solutions’ encryption methods. By determining the efficiency of these secure cloud storage providers’ encryption methods, it can help others determine which of these providers will fit their needs. To determine efficiency, the CPU, memory, and network usage of these cloud storage solutions will be measured at idle and when files are being uploaded to them. To measure the CPU, memory, and network usage, ‘dstat’ will be used to capture their usages and record them to a CSV file. It is expected that both cloud solutions will have better performance when they have files being uploaded to them with no encryption. However, with the encryption methods of both cloud solutions being lightweight, that was not the case.

Secure Cloud Storage with Deduplication Technique

Data de-duplication refers to providing cloud providers with a way to manage the uncontrollable data and the challenges of cloud storage. The success of IOT and social media led us to face big data challenges. Big data is interesting but also becomes a critical challenge for cloud service providers. Data storage and management become also the topic of discussion where big data generate business opportunities as well as come with big issues for the cloud providers. In this paper, we discussed the issues of redundant data and techniques to prevent data redundancy on the cloud. A third-party auditor checks the user’s data for correctness and gives the accuracy of the data that is stored in a cloud server. The communication and computation overheads were reduced. The deduplication technique is used to check whether the file that users need to store in cloud storage already exists on the cloud server.

Revolutionizing Cloud Storage Security: Searchable Public-Key Encryption

The Secure Cloud Storage App, developed using React.js for the frontend and MongoDB for thebackend, addresses the growing need for a reliable and privacy-focused solution for storing and managing digital assetsin the cloud. In response to the escalating demand for a dependable and privacy-centric solution to handle digital assetsin the cloud, the Secure Cloud Storage App has emerged, meticulously crafted with React.js powering its frontend andMongoDB driving its backend architecture. This innovative application stands as a beacon for users seeking a securehaven for their data. At its core, security takes precedence, with the implementation of cutting-edge encryptionprotocols meticulously woven into every aspect of data transmission and storage. This fortification ensures the utmostconfidentiality and integrity of user information throughout its journey within the cloud infrastructure. The userinterface, expertly fashioned using React.js, stands as a testament to seamless design and intuitive interaction. Users areempowered with effortless capabilities to upload, organize, and access their files, enhancing productivity andconvenience in managing digital assets. MongoDB, a stalwart in backend database solutions, offers a robust foundationcharacterized by scalability and flexibility, adeptly catering to diverse storage requirements. Moreover, the SecureCloud Storage App encompasses comprehensive user authentication and authorization mechanisms, bolstering accesscontrol and privacy measures. Through the amalgamation of secure authentication protocols and end-to-end encryption,paramount importance is placed on shielding user data from prying eyes, fostering an environment of trust andconfidence in cloud-based file management. The synergy between React.js and MongoDB transcends meretechnological integration; it epitomizes a harmonious marriage of functionality and security. Not only does this pairingensure a responsive and dynamic user interface, but it also lays down a resilient and secure infrastructure for the storageand retrieval of sensitive information within the cloud ecosystem. In essence, the Secure Cloud Storage App stands as abeacon of innovation, ushering in a new era of secure and efficient cloud-based file management solutions.

SECURED ORGANIZATIONAL CLOUD STORAGE USING MESSAGE DIGEST ALGORITHM

The Secure Organizational Cloud Storage System is an advanced cloud storage platform designed specifically for organizations that prioritize data security. The platform provides a similar functionality to Google Drive, but with enhanced security features to ensure that sensitive information remains safe from cyber threats. With the Secure Organizational Cloud Storage System, organizations can store and manage their data in a centralized and secure location. Users can easily upload, share, and collaborate on files and documents, and can access them farom anywhere with an internet connection. Secure cloud storage is indispensable in today's data-driven world, providing the convenience of remote data access while mitigating the risks associated with cyber threats and data breaches. By implementing advanced encryption, access controls, data integrity checks and redundancy strategies, secure cloud storage systems offer a comprehensive solution to protect sensitive information in an increasingly interconnected digital ecosystem. This abstract serves as a concise introduction to the importance and key components of secure cloud storage. Cloud service and storage providers offer valuable IT solutions for businesses of all sizes. Originally thought of as more for personal use, cloud storage for business is following in the footsteps of many personal technologies adapted for business purposes. Cloud storage works by storing data on remote servers, where it can be maintained, managed, backed up and accessed remotely. Data stored in the cloud is accessible by any device at any time, as long as permissions are in place. Despite its accessibility, data stored via the cloud is extremely safe and secure. Many people reap the benefits of the cloud for personal reasons, but most businesses have yet to take the leap. Whether your current on-premises data storage seems sufficient, or you simply haven’t taken time to consider cloud storage, reviewing these advantages is a good place to start.

Multi User Authentication for Reliable Data Storage in Cloud Computing

Today's digital environment, Multi-user authentication plays a crucial role in ensuring data integrity and confidentiality, emphasizing its importance of reliable and secure data storage in cloud computing environments. The exploration extends to the strategies for implementing secure multi-user authentication, encompassing aspects such as password policies, biometric verification, encryption, role-based access control (RBAC), and multi-factor authentication (MFA). The issue of reliable data storage is covered in further detail, on the importance of data availability and integrity. Real-world applications of multi-user authentication and reliable data storage are examine. The paper elucidates how these applications enhance overall security, mitigating risks associated with unauthorized access and cyber threats.The paper concludes by integration of multi-user authentication and reliable data storage is explored through considerations the critical role of multi-user authentication in ensuring reliable data storage in cloud computing such as secure API access, token-based authentication, and adherence to security best practices. Challenges in user authentication are addressed, with solutions proposed for seamless access across cloud platforms, including the adoption of Single Sign-On (SSO), multi-factor authentication, regular security audits, collaboration with cloud security experts, and user education and training. The synthesis of challenges, benefits, drawbacks, and implementation strategies provides organizations with a comprehensive guide for enhancing their data security measures.

An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.

Integrating IoT with WSNs using fuzzy logic-based middleware

With the rapid adoption of cloud storage for business and personal use, data security has become a significant concern. This study investigates the effectiveness of advanced encryption algorithms to ensure the integrity, confidentiality, and availability of data stored in cloud environments. The research focuses on the implementation and evaluation of three encryption algorithms: AES-256, ChaCha20, and Threefish, comparing their performance in terms of computational complexity, key generation, and resistance to various attacks. The study utilizes a testbed consisting of a simulated cloud storage environment, where the encryption algorithms are deployed and assessed based on encryption/decryption time and throughput. Results indicate that the ChaCha20 algorithm outperforms both AES-256 and Threefish in terms of encryption/decryption speed while maintaining strong security. Moreover, the findings suggest that the combination of these encryption algorithms can enhance data security by providing a multi-layered defense mechanism against potential threats. The research contributes to the advancement of cloud storage security by identifying optimal encryption algorithms and proposing a robust solution for safeguarding sensitive information.

Enhanced Privacy for Health Records Using Blockchain Technologies

This project deals with the security of digital medical history by implementing advanced measures such as encryption using blockchain methods, access control. Thanks to the integration of encryption, blockchain, dynamic access control and security even after decryption, a robust privacy framework is built to meet evolving cyber threats and strengthen security and availability of health data. This conventional method of storage deprives doctors and researchers of capitalizing on modern technologies and healthcare engines to perform clinical tests, improve patient care, and contribute to future clinical research [1]. By empowering patients using blockchain technology, this project is transforming healthcare data management to improve patient care and promote the development of a reliable healthcare environment, which means remarkable progress towards improving Privacy for Health Records Using Blockchain Technologies. Key Words: Cloud Storage Security, Blockchain Technology, Health Records, Privacy and Security, Blockchain Algorithms, Keccak, Keras, Tesseract