Cloud Data Storage Research Articles

HIDE-Healthcare IoT Data Trust ManagEment: Attribute centric intelligent privacy approach

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.

Enhancing privacy‐preserving mechanisms in Cloud storage: A novel conceptual framework

SummaryData privacy is critical for users who want to use Cloud storage services. There is a significant focus on Cloud service providers to address this need. However, in the evolving dynamic cyber‐space, privacy infractions are rising and pose threats to Cloud storage infrastructures. Several studies developed various models and techniques to ensure the privacy of Cloud storage contents. However, these models came with several shortages in the privacy‐preserving attributes they cover. Thus, this article identified a comprehensive set of Cloud data storage privacy‐preserving attributes to propose a flexible and efficient framework to handle the privacy problem. This framework uses a multi‐layer encryption storage structure and a one‐time password authentication technique. The findings of this article intend to help future communities to enhance existing techniques or develop new research‐based practical alternatives. Since Cloud computing is a rapidly growing technology, new privacy vulnerabilities emerge daily. Future research might confirm the findings of this article and test the suggested framework in different contexts.

Searchable encryption over encrypted speech retrieval scheme in cloud storage

Aiming at the privacy and security issues of sensitive speech data in cloud storage, and how to achieve efficient retrieval with privacy-preserving for encrypted speech data in cloud storage, a searchable encryption over encrypted speech retrieval scheme in cloud storage was proposed. Firstly, the data owner encrypts the original speech data adopting symmetric encryption algorithm Lorenz chaotic mapping. Secondly, the encrypted speech data is uploaded to the cloud server for storage, and meanwhile the Mel frequency cepstrum coefficient (MFCC) features of original speech are extracted as the input of the convolutional neural network (CNN) to execute the deep semantic feature extraction, which is taken as the speech keywords. Finally, the extracted speech keywords are encrypted and stored in the cloud. When the authorized user sends a retrieval request, the trained CNN is utilized to extract the keywords of the speech to be retrieved, and the search trapdoor is generated and sent to the cloud server. In the process of retrieval, the Euclidean distance is used to match the encrypted keywords with the search trapdoor. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy, and can be suitable for the encrypted storage of speech data and efficient secure retrieval.

SecDedup: Secure data deduplication with dynamic auditing in the cloud

In the era of big data, data explosion has brought challenges to cloud storage management. To improve cloud storage efficiency and save network communication bandwidth, cloud data deduplication has emerged as a research hotspot, especially in the field of encrypted cloud data storage. How to enhance the security of encrypted data deduplication by resisting various attacks on deduplication has become an important research issue. However, existing solutions suffer from security flaws and are vulnerable to a series of attacks, e.g., duplicate faking attacks, file ownership spoofing attacks, and file tampering attacks. Besides, dynamic data operation is rarely considered or audited. To solve the above problems, we propose a novel scheme, named SecDedup, to enhance the security of encrypted cloud data deduplication with dynamic auditing. SecDedup applies a homomorphic authenticator and designs a multi-functional data tag with optimized storage to support deduplication and auditing at the same time with security guarantee against various attacks as mentioned above. In particular, We embed multi-set hash functions into data tags to achieve dynamic data auditing. In addition, SecDedup supports batch auditing with optimized computational cost for multiple deduplication auditing tasks. We formally prove the correctness and security of SecDedup, showing that it can successfully achieve our design goals for resisting the above listed attacks. We also analyze and evaluate the performance of SecDedup in terms of computation, communication, and tag storage overheads by comparing them with existing works. The results show its effectiveness and scalability.

Blockchain-Assisted Comprehensive Key Management in CP-ABE for Cloud-Stored Data

Blockchain-Assisted Comprehensive Key Management in CP-ABE for Cloud-Stored Data

A Review of Assured Data Deletion Security Techniques in Cloud Storage

Cloud computing is an interesting technology that allows customers to have convenient, on-demand network connectivity based on their needs with minimal maintenance and contact between cloud providers. The issue of security has arisen as a serious concern, particularly in the case of cloud computing, where data is stored and accessible via the Internet from a third-party storage system. It is critical to ensure that data is only accessible to the appropriate individuals and that it is not stored in third-party locations. Because third-party services frequently make backup copies of uploaded data for security reasons, removing the data the owner submits does not guarantee the removal of the data from the cloud. Cloud data storage has grown in popularity, and the problem of ensured deletion has been solved. Several schemes to overcome the assured deletion problem have been proposed over the last few years. The proposed solutions have addressed the scaling overhead, trusted third parties, delays, single points of failure, and other inefficiencies. Customers had the option of receiving verifiable proof of deletion from cloud service providers. This article focuses on the issue of how cloud data storage clients may be confident that the deleted data from the cloud cannot be recovered. Furthermore, it discusses the practice of secure deletion. Moreover, the paper explores currently used methods to achieve the security of assuring the deletion of data faced by cloud entities such as cloud service providers, data owners, and cloud users. After that, the paper analyzes techniques to find the pros and cons of assured deletion and the problems that were solved by these techniques. Finally, the paper identifies some future directions for the development of assured deletion of cloud storage.

Secure cloud data storage solution with better data accessibility and time efficiency

The environment of cloud computing provides several advantages and a variety of data storage models, which entirely frees users from the vexing processes of storage equipment upgradation and data administration. Nevertheless, the customers’ primary worry is the safety of their data, and the literature offers a number of different security-based solutions for addressing this issue. This paper proposes a technique for time-efficient cloud data storage that makes use of keccak and Advanced Encryption Standard (AES) which enhances the data availability with a reasonable storage space. Third party agents are not employed, due to the fact that a third party cannot be relied upon to maintain a high level of data security. Thus, the proposed algorithm is applied by cloud users before the process of outsourcing is carried out. This method is resistant to data tamper and analytical attacks. In addition, the execution of the work that has been proposed requires a limited amount of time, which, in turn, minimizes the amount of energy that is required. In contrast to the already used algorithms, the presented work demonstrates superior performance in terms of its overall effectiveness.

Framework Design of an Edge Gateway System Supporting Multi-Protocol Standardized Access Detection

Recently, intelligent city construction has been promoted with the development of the Internet of things (IoT). The edge IoT gateway plays a critical role as the data aggregation core and processing center. Most existing gateways mainly solve heavy data storage and processing loads in cloud computing centers. There is less attention paid to multi-protocol data transmission and fusion. However, multiple products with different protocols in an IoT system require a flexible gateway compatible with multiple protocols. This paper proposes a multi-protocol edge gateway. The frame design was based on the actual demand for edge data acquisition. The gateway hardware platform used an RK3399 chip transplanted from the embedded operating system. It could support simultaneous multi-protocol access to ZigBee, LoRa, Bluetooth, and Wi-Fi. We combined the plug-and-play (PnP) hardware device access detection scheme with the system onboard interface driver to realize dynamic access detection and unified device management. In addition, the gateway also integrated data storage and access functions and partial edge computing functions. Finally, the experiment results verified that the multi-protocol edge gateway could meet the demand for data access and device control.

Guest Editorial: Blockchain and decentralised solutions for social good

Guest Editorial: Blockchain and decentralised solutions for social good

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment

With the increasing rise of distributed system technologies, one of the most pressing problems facing the digital world is ensuring the security of sensitive and confidential data during transport and storage, which is also regarded as one of the most critical difficulties facing cloud computing. Numerous techniques exist for enhancing data security in the cloud computing storage environment. Encryption is the most important method of data protection. Consequently, several accessible encryption strategies are utilized to provide security, integrity, and authorized access by employing modern cryptographic algorithms. Cloud computing is an innovative paradigm widely accepted as a platform for storing and analysing user data. The cloud is accessible via the internet, exposing the data to external and internal threats. Cloud Service Providers (CSPs) must now implement a secure architecture to detect cloud intrusions and safeguard client data from hackers and attackers. This paper combines Stochastic Gradient Descent long short-term memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. User registration, intrusion detection, and intrusion prevention are the three phases of the planned system. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. In the data access phase, cloud data access is managed by authenticating the authorized user with the Blowfish encryption algorithm. Comparing the proposed classifier to existing classifiers demonstrates that it detects abnormal access accurately. The experimental outcomes enhanced data security, which can be utilized to protect cloud computing applications. The experimental results of the suggested SGD-LSTM algorithm indicated a high level of protection, as well as a considerable improvement in security and execution speed when compared to algorithms that are often used in cloud computing.

Hybrid HP-BOA: An Optimized Framework for Reliable Storage of Cloud Data Using Hybrid Meta-Heuristic Algorithm

The prime objective of the cloud data storage process is to make the service, irrespective of being infinitely extensible, a more reliable storage and low-cost model that also encourages different data storage types. Owing to the storage process, it must satisfy the cloud users’ prerequisites. Nevertheless, storing massive amounts of data becomes critical as this affectsthe data quality or integrity. Hence, this poses various challenges for existing methodologies. An efficient, reliable cloud storage model is proposed using a hybrid heuristic approach to overcome the challenges. The prime intention of the proposed system is to store the data effectively in the cloud environment by resolving two constraints, which are general and specific (structural). The cloud data were initially gathered and used to analyze the storage performance. Since the data were extensive, different datasets and storage devices were considered. Every piece of data was specified by its corresponding features, whereas the devices were characterized by the hardware or software components. Subsequently, the objective function was formulated using the network’s structural and general constraints. The structural constraints were determined by the interactions between the devices and data instances in the cloud. Then, the general constraints regarding the data allocation rules and device capacity were defined. To mitigate the constraints, the components were optimized using the Hybrid Pelican–Billiards Optimization Algorithm (HP-BOA) to store the cloud data. Finally, the performance was validated, and the results were analyzed and compared against existing approaches. Thus, the proposed model exhibited the desired results for storing cloud data appropriately.

Backward Compatible Identity-Based Encryption.

In this paper, we present a new identity-based encryption (IBE) system that is named Backward Compatible Identity-based Encryption (BC-IBE). Our BC-IBE is proposed to solve the problem caused by the out-of-synchronization between users' private keys and ciphertexts. Encryption systems such as revocable IBE or revocable Attribute-based Encryption (ABE) often require updating private keys to revoke users after a certain time period. However, in those schemes, an updated key can be used to decrypt the ciphertexts created only during the current time period. Once the key is updated and the previous keys are removed, the user, the owner of the updated key, will lose access to the past ciphertexts. In our paper, we propose BC-IBE that supports backward compatibility, to solve this problem. In our proposed system, user's private keys and ciphertexts can be updated periodically with time tags, and these processes can be used to revoke users who do not receive an updated key as the other revocable encryption does. However, in our proposed system, a private key newly issued to a user is backward compatible. This means that it decrypts not only the ciphertexts at the present time period but also all past ciphertexts. This implies that our proposed scheme guarantees the decryption of all encrypted data even if they are not synchronized. Compared to the existing revocable identity-based encryption system, our proposed BC-IBE has the advantage of simplifying key management and securely delegating ciphertext updates. Our proposed scheme only requires a single backward-compatible private key to decrypt all past ciphertexts created. Moreover, the ciphertext update process in our proposed scheme does not require any special privileges and does not require decryption. This means that this process can be securely delegated to a third-party server, such as a cloud server, and it prevents the potential leakage of secrets. For those reasons, BC-IBE is suitable for a system where users are more dynamic, such as the Internet-of-Things (IoT) network, or a system that regularly updates the data, like cloud data storage. In this paper, we provide the construction of BC-IBE and prove its formal security.

Designing attribute-based verifiable data storage and retrieval scheme in cloud computing environment

The cloud computing technology is a novel storage and computing paradigm that enables individuals and organizations to store data, share data with intended group of users and retrieve data when require. It greatly improves peoples’ data storage and sharing, and data retrieval capabilities by providing flexible, less expensive and quality services. For data security and privacy concerns, secure and authenticated data storage, fine-grained access control of encrypted data, secure search for the outsourced data and search results verification are of critical importance. However, achieving the aforementioned functionalities simultaneously is quite challenging. In this paper, for the first time, we propose a secure lightweight Attribute-Based verifiable Data Storage and data Retrieval Scheme (ABDSRS) for cloud environments that attains the following features: (i) lightweight design, (ii) provably secure, (iii) fine-grained data access control, (iv) data owner (DO) anonymity, (v) data and DO authenticity, (vi) keyword policy search over encrypted data, (vii) keyword privacy, and (viii) search results verification. ABDSRS employs attribute-based online-offline mechanism in which only authorized DOs can anonymously upload data to the cloud. And, a data user (DU) can search over encrypted data using keyword policy. ABDSRS enables a DU to verify the correctness of the search results (i.e., the correctness of the operations performed by the cloud) without interacting with any authority. ABDSRS is lightweight in the sense that the heavy computations are offloaded either to the cloud or to offline phase, while only lightweight operations are executed at the DU device. We formalize more general security definitions of ABDSRS by considering various possible adversarial capabilities and present rigorous security analysis. We also conduct experiments to evaluate ABDSRS’s performance.

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis.

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls for IoT

Devices in the Internet of Things (IoT) usually use cloud storage and cloud computing to save storage and computing cost. Therefore, the efficient realization of one-to-many communication of data on the premise of ensuring the security of cloud storage data is a challenge. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can not only protect the security of data in the cloud and achieve one-to-many communication but also achieve fine-grained access control for data. However, the single-authority CP-ABE faces the crisis of single point of failure. In order to improve security, the Multi-Authority CP-ABE (MA-CP-ABE) is adopted. Although there are provably-secure MA-CP-ABE schemes, Edward Snowden's research shows that provably-secure cryptographic schemes are vulnerable to backdoor attacks, resulting in secret disclosure, and thus threatening security. In addition, ABE requires huge computational overhead in key generation, encryption and decryption, which increase with the increase in the number of attributes and the complexity of the access structure, and there are a large number of resource-constrained devices in the IoT. To mitigate this issue, we construct the Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls (OO-MA-CP-ABE-CRFs) scheme. This scheme not only uses Cryptographic Reverse Firewall (CRF) to resist backdoor attacks but also uses online/offline key generation, online/offline encryption and outsourcing encryption technology to optimize the efficiency of the MA-CP-ABE scheme with reverse firewall, reducing the storage and computing cost of users. Finally, the security of the OO-MA-CP-ABE-CRFs scheme is proved, and the experimental results indicate that the scheme is efficient and practical.

Study of Efficient Cloud Storage Architectures for the Security Environment

Lately, cloud computing has acquired notoriety. Data storage is an essential and beneficial area of concentrate in cloud computing. The term "cloud data storage" describes the process of transferring data to an external, up-to-date storage system. You store data to a far-off database instead of your PC's hard plate or another nearby storage gadget. Your PC and the database are associated over the web. The user's requirements for hardware and software are reduced via cloud storage. The security issues associated with accessing and storing data on cloud storage render the cloud information inconsistent and untrustworthy. Through the internet, the cloud computing provides service with dynamically scalable resources. Users gain from cloud computing services in terms of cost and usability. Security issues must be addressed by cloud computing services when sending sensitive data and important applications to shared and public cloud environments. For the needs of data processing and storage, cloud environments are expanding up significantly. The use of cloud computing has a number of benefits as well as drawbacks for service users' data security. The primary security concerns that exist in cloud computing systems are highlighted in this study. The essential security worries with cloud computing are examined in this paper, alongside a straightforward, secure, and protection saving architecture for between cloud data sharing that depends on an encryption/unscrambling calculation that expects to shield data put away in the cloud from unapproved access.

An Empirical study of Hybrid Cryptographic Algorithms

For the last few decades cloud computing is a blooming word in the field of computer science. Cloud computing is a fast growing technology; it provides various services to the user through internet on demand. Now a days, people in busy move, they use Cloud to store and retrieve data at anywhere, any time without use of any physical storage devices like pen drive, compact disc etc . Enormous features of cloud, most of the small and large scale organizations outsource their data in cloud data storage. With the widespread application of cloud, huge amount of users are incorporated in public cloud, it may lead to vulnerable attacks. So security and privacy is an important factor in cloud environment. This security problem can be solved by various ways. Cryptography is one of the techniques to secure user data in cloud. Researchers can use various cryptographic algorithms to implement the security in cloud storage. This paper focuses the summative analysis of researches, in cloud security from 2018-2022. This survey paper provides solution to researchers who have their work in cloud.

A robust VC scheme for securing cloud data storage

Visual Cryptography (VC) was created to encode images into several shares and decode them by aggregating the shares without the use of costly standard cryptanalysis. To enhance the quality and security of visual images in cloud storage, a Robust Collusion Avoidance-based Secure Signific VC (RCA-SSVC) scheme was designed, which creates the collusion-resistance Secret Image (SI) shares and embeds them into the Cover Image (CI) to prevent collusion threats. But it takes a long-time during access for users to recover the real image whenever receiving fake shares kept in the cloud by unauthorized users. Therefore, in this article, a new 2-level data protection scheme is proposed using QR code and RCA-SSVC. In this scheme, the public and private information of users are taken as QR code, which is encoded into sub-pixel blocks of all shares during the share creation phase. Also, a random permutation is applied to the created shares to disrupt the sub-pixels in a single block, which can avoid attackers from guessing the location of the Secret QR (SQR) code’s pixels. During the image restoration step, the restoration process is initiated once the user identity is authenticated by reading QR code information, which is matched with the QR code embedded in the shares. So, image restoration using illegitimate shares during access is prevented. At last, the test outcomes reveal that the RCA-SSVC-QR establishes maximum confidentiality in image access from the cloud storage compared to the classical VC schemes.

SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure DataStorage in Cloud

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan, etc are the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity, etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.