Cloud Data Storage Research Articles

Cloud Storage Data Security and Integrity Protection Using Localized and Customized Key Manipulator (LCKM) Algorithm

Cloud Storage Data Security and Integrity Protection Using Localized and Customized Key Manipulator (LCKM) Algorithm

Online/offline remote data auditing with strong key-exposure resilience for cloud storage

Remote Data Auditing (RDA) with key-exposure resilience enables secure cloud data storage both earlier and later than the key exposure happens. However, most of existing solutions suffer from the security attack during the key-exposed time period, meaning the cloud server can still discard or tamper with the data owner’s files in the key-exposure period. While others require a secure channel for key update, which is unrealistic and expensive under key-exposure scenarios. In this paper, we construct an online/offline RDA framework with strong key-exposure resilience, called S-OORDA, which can resist strong key-exposure attacks and achieve the integrity verification for cloud data efficiently. Then based on the framework, we present a concrete S-OORDA scheme supporting the data owner’s secret key evolution without the secure channel. And the authenticators can be updated remotely at each time period, so as to prevent the attackers to forge the authenticators using the exposed auditing secret keys. Meanwhile, the data auditing process of the proposed scheme is divided to online and offline phases, greatly relieving the online computational burden of both the data owner and the third-party auditor. Security and performance analysis confirm that the proposed scheme meets the desired security and has the optimized efficiency.

Secure Cloud Storage and Retrieval of Personal Health Data From Smart Wearable Devices With Privacy-Preserving Techniques

With the increasing awareness of personal health, personal health data management has become an important part of people's lives. Smart wearable devices (SWDs) collect people's personal health data, and then store the data on cloud. Authorized entities access the data to provide personalized health services. However, these personal health data contain a large amount of sensitive information, which may pose a significant threat to people's lives and property. To address this, this paper proposes a privacy-preserving solution. SWD data is encrypted, and secure indexes are created using Bloom filter and 0-1 encoding. Encrypted data and indexes are stored in a semi-trusted cloud. Only authorized entities can access the ciphertexts, ensuring secure personalized health management. Extensive experiments validate the scheme's efficiency in index construction, query token generation, and ciphertext search. Security analysis confirms no external entity, including the cloud, gains additional information during retrieval.

Hypermedia-based software architecture enables Test-Driven Development.

Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud. Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort. After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage. Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly. Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.

Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography

Technology for Cloud Computing (CC) has advanced, so Cloud Computing creates a variety of cloud services. Users may receive storage space from the provider as Cloud storage services are quite practical; many users and businesses save their data in cloud storage. Data confidentiality becomes a larger risk for service providers when more information is outsourced to Cloud storage. Hence in this work, a Ciphertext and Elliptic Curve Cryptography (ECC) with Identity-based encryption (CP-IBE) approaches are used in the cloud environment to ensure data security for a healthcare environment. The revocation problem becomes complicated since characteristics are used to create cipher texts and secret keys; therefore, a User revocation algorithm is introduced for which a secret token key is uniquely produced for each level ensuring security. The initial operation, including signature, public audits, and dynamic data, are sensible to Sybil attacks; hence, to overcome that, a Sybil Attack Check Algorithm is introduced, effectively securing the system. Moreover, the conditions for public auditing using shared data and providing typical strategies, including the analytical function, security, and performance conditions, are analyzed in terms of accuracy, sensitivity, and similarity.

Blockchain-based integrity auditing for shared data in cloud storage with file prediction

With the increasing publicity of information, more and more users are willing to share their data with others. Therefore, in cloud storage services, the security of shared data has begun to attract attention. Most of the current integrity auditing schemes cannot balance data sharing and audit security well. In view of this, we propose an audit scheme based on blockchain and file prediction in the shared environment. On the one hand, a lightweight signature algorithm is designed to realize the revocation of group members without source data transmission, which greatly avoids the communication overhead between the user and the cloud. On the other hand, we introduce blockchain technology into auditing and use smart contracts to verify the integrity of cloud data, making the system more secure, tamper-resistant, and traceable. Finally, in order to ensure that a file is audited only when necessary, we propose the concept of valid auditing and predict the frequently accessed or likely accessed files in the sharing group, which greatly improves the valid auditing rate and saves system computing resources. The security analysis proves that our method is safe and reliable. The performance evaluation and experimental comparison prove that our scheme has high efficiency.

IoT-Applicable Generalized Frameproof Combinatorial Designs

Secret sharing schemes are widely used to protect data by breaking the secret into pieces and sharing them amongst various members of a party. In this paper, our objective is to produce a repairable ramp scheme that allows for the retrieval of a share through a collection of members in the event of its loss. Repairable Threshold Schemes (RTSs) can be used in cloud storage and General Data Protection Regulation (GDPR) protocols. Secure and energy-efficient data transfer in sensor-based IoTs is built using ramp-type schemes. Protecting personal privacy and reinforcing the security of electronic identification (eID) cards can be achieved using similar schemes. Desmedt et al. introduced the concept of frameproofness in 2021, which motivated us to further improve our construction with respect to this framework. We introduce a graph theoretic approach to the design for a well-rounded and easy presentation of the idea and clarity of our results. We also highlight the importance of secret sharing schemes for IoT applications, as they distribute the secret amongst several devices. Secret sharing schemes offer superior security in lightweight IoT compared to symmetric key encryption or AE schemes because they do not disclose the entire secret to a single device, but rather distribute it among several devices.

SOCIALIZATION OF THE USE OF GOOGLE DRIVE AT PUSKESMAS AMBACANG KOTA PADANG

Computer technology has developed significantly, from Windows application programs to increasingly sophisticated data storage media. In the past, we usually stored data using diskettes with small storage sizes, then Flashdisk and Hardisk appeared, until the entry of a new era, namely Cloud storage technology. Cloud technology itself has broad functions, one of which is as an online data storage medium. Like the familiar cloud-based data storage service, Google Drive can store up to 15GB of data for free. In addition to data storage like Google Drive, there is also cloud data storage, such as DropBox, OneDrive, and others. By using Google Drive, users experience many benefits, including being able to share data with those who need data. There is no need to store files on a flash disk or hard disk, where our files are directly stored on Google servers, which can be used at any time, modified. In using Google Drive, many Ambacang Health Center staff do not understand Google Drive. The purpose of holding this Community Service is so that all staff can understand Google Drive how to use it and its features, the results achieved are that all participants understand and understand the use of Google Drive. This socialization has been carried out 2 times, on May 27 and June 17, 2023, with lecture and discussion methods.

A Security Method for Cloud Storage Using Data Classification

Cloud computing is an information technology model that provides computing and storage resources as a service. Data storage security remains the main challenge in adapting this new model. The common solution to secure data in the cloud is data encryption. However, handling all the data with the same security policy does not appear to be good practice, because they do not have the same sensibility for the data owner. The present research proposes a new method to improve the security of data in cloud storage. It combines two domains represented by machine learning and multi criteria decision making, in order to provide a new classification method, that classifies data before being introduced into a suitable encryption system according to their category. A Cloudsim simulation has been used to demonstrate the effectiveness of the proposed method. The results of the simulation exhibit that our method is more efficient and accurate and takes less processing time, while ensuring data confidentiality and integrity.

Go Science Drone Med Box – Medical Emergency Drone for Emergency Healthcare Management

The Medical Emergency Drone Box stands as a transformative development in healthcare technology, designed to address a spectrum of emergency medical needs. This paper delineates the comprehensive capabilities of the device, which ranges from its potential role in rural healthcare support to its utilization in critical emergencies, road accidents, and even organ transportation. The device integrates advanced sensors, such as body temperature and pulse detection units, to facilitate real-time monitoring and data-driven decision-making during medical interventions. It also incorporates a WiFi module and microcontrollers for efficient data transmission and seamless connectivity. To ensure the maintenance of required medical standards, the drone box includes a heating and cooling system for preserving critical medical supplies. The cloud-based storage of sensor data allows healthcare professionals to remotely access vital information in real-time. Moreover, the device is built to operate effectively even in regions with limited electrical access, courtesy of its robust battery backup system. Additional features include charging units and an assortment of essential medical equipment like portable defibrillators and drilling machines. Solar Panels are also added as a redundant power support. This paper aims to showcase the multifaceted features, data-centric functionalities, and the enormous potential the Medical Emergency Drone Box holds in revolutionizing emergency healthcare delivery across diverse settings. Developed by Go Science Technical Team. IndexTerms – Go Science, Medical Emergency Drone Box, Healthcare Innovation, Critical medical Interventions, Sensor Technology, Real-Time Data Monitoring, Data Transmission, Battery Back-up, Rural healthcare, Road Accidents, Organ Transportation, Cloud Data Storage, Portable Medical Equipment.

Fair Cloud Auditing Based on Blockchain for Resource-Constrained IoT Devices

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> air <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> loud <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> uditing proposal by employing the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> lockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">honest</i> cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.

Public Auditing with Secure Team Management for Shared Data in the Cloud with Maintaining the Confidentiality

Abstract: The cloud computing based public auditing data preserving refers to the process of conducting audits on data stored in the cloud to ensure its integrity, authenticity, and confidentiality are maintained. It involves allowing external entities, such as auditors or regulators, to verify the correctness of data without compromising its security. In this paper, we proposed a scheme for shared data that supports privacy, identity traceability and group dynamics. Cloud storage auditing is an extremely important technique for resolving the problem of ensuring the integrity of stored data in cloud storage. This scheme is secure against collusion attacks between CSPs and revoked users. This concept is for public auditing with secure group management. This scheme is useful for the people auditing purpose in this scheme the auditing details the auditor will upload the files to the cloud and the one group management will maintain a detail in the secure management. This group contains Third party auditor one who uploads the auditing details, a Group manager one who manages the group, Group members who work in that group and Cloud Service Provider that is providing the cloud service to the group management and will maintain the data. In this, the security will be provided to the group manager and group members so that the information will be secure before as by OTP process and after the data will be secure through attribute-based encryption of the process

A secure framework for managing data in cloud storage using rapid asymmetric maximum based dynamic size chunking and fuzzy logic for deduplication

A secure framework for managing data in cloud storage using rapid asymmetric maximum based dynamic size chunking and fuzzy logic for deduplication

Trust aware cryptographic role based access control scheme for secure cloud data storage

Cloud data storage lets customers store vast amounts of data cheaply on demand. Cryptographic role-based access control (RBAC) systems preserve cloud data privacy by restricting access to users. This study develops a trust model to reason about and improve data security in cryptographic RBAC cloud storage systems. The trust degrees of the user determine the access rights to the data and are performed by User Activity Monitoring Agent (UAMA). Two different misconducts of users such as access policy violation and data leakage affect the trust degree of the user, which in turn upgrades the access policy. In addition, the user has to decrypt the data for gaining information from it, which is a second line of security. The performance of trust based RBAC scheme is evaluated with respect to different parameters such as illegitimate user detection, memory consumption, data storage with retrieval time and the proposed work performs better.

Secure and Distributed IoT Data Storage in Clouds Based on Secret Sharing and Collaborative Blockchain

Secure and Distributed IoT Data Storage in Clouds Based on Secret Sharing and Collaborative Blockchain

Construction of Singleton-Type Optimal LRCs from Existing LRCs and Near-MDS Codes

Modern large scale distributed storage systems play a central role in data center and cloud storage, while node failure in data center is common. The lost data in failure node must be recovered efficiently. Locally repairable codes (LRCs) are designed to solve this problem. The locality of an LRC is the number of nodes that participate in recovering the lost data from node failure, which characterizes the repair efficiency. An LRC is called optimal if its minimum distance attains Singleton-type upper bound [1]. In this paper, using basic techniques of linear algebra over finite field, infinite optimal LRCs over extension fields are derived from a given optimal LRC over base field (or small field). Next, this paper investigates the relation between near-MDS codes with some constraints and LRCs, further, proposes an algorithm to determine locality of dual of a given linear code. Finally, based on near-MDS codes and the proposed algorithm, those obtained optimal LRCs are shown.

Cloud security: literature survey

&lt;span lang="EN-US"&gt;Today, the growth of digitalization has made the ease for livelihood for all the organizations. Cloud computing the storage provider for all the computer resources has made it easy for accessing the data from anywhere anytime. But at the same time the security for cloud data storage is the major drawback which is provided by various cryptographic algorithms. These algorithms convert the data into unreadable format, known as cipher text, Rivest, Shamir and Adleman (RSA) one of the most popularly used asymmetric algorithm. This paper gives detailed review about such different cryptographic algorithms used for the cloud data security. The comparison study is also made for the size of data and to analyze the encryption time and decryption time, which concludes that to enhance the cloud data security some addon techniques are to be used along with these cryptographic algorithms. To increase the security level and to increase the transmission speed of plaintext, integrated method will be proposed by encoding the plaintext to intermediate plaintext and then intermediate plaintext will be compressed using any one of the compression techniques to increase the compression ratio, lastly the compressed file is encrypted to further enhance the security level.&lt;/span&gt;

The Infallible and the Specter – Manifesting (artificial) subjectification in female sex robots

The text outlines a recent artistic practice and theoretical research into a female AI sex doll object, its materiality, and signification. It is a culmination of a three-year study and intervention into the coded systems of control and sites of resistance that play out within the context of an artificial female body. Machine Learning (ML) algorithm is the mediator between sex robots, their users and cloud data storage, facilitating learning from their inter-actions. I examine this engagement and materiality of the sex robot through notions of feminist mimesis, substitute and simulation against emancipatory politics in object formation. I connect these ideas with theories of new materialism and recent scholarship on digital data science.

ANALYSIS OF THE QUALITY OF CLOUD STORAGE SERVICES ON NEXTCLOUD AND PYDIO

The development of information technology has greatly influenced the development of current storage media, such as cloud-based storage (cloud). Nextcloud and pydio storage are part of an open based cloud computing system. Measuring service quality can be seen from the aspects of throughput, delay, jitter, and packet loss. This research was conducted to compare the upload and download speeds between the nextcloud server and the pydio server. Of the two cloud storage servers, five trial upload and download respectively. Based on the test results showing that the upload and download processes produce final cloud storage data which has the highest average throughput in terms of uploading files of different types and capacities, the next cloud server is 1,934.6 kbps. The cloud storage that has the lowest average packet loss value is balanced, because the two cloud storages get the same packet loss value of 0%. The cloud storage that has the lowest average file upload delay is the nextcloud server at 222,585 ms. The cloud storage that gets the lowest jitter value is the pydio server of 160.56 ms. Meanwhile, for downloading cloud storage files that have the highest average throughput value, nextcloud server is 1,332 kbps. The cloud storage that has the lowest average packet loss value is balanced, because the two cloud storages get the same packet loss value of 0%. The cloud storage that has the lowest average download delay is the nextcloud server at 132,057 ms, while the cloud storage that has the lowest jitter is the nextcloud server at 132,139 ms.

Retracted: Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage

Retracted: Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage