DPM — efficient storage in diverse environments

To balance performance and storage efficiency, modern clustered file systems often first store data with replication, followed by encoding the replicated data with erasure coding. We argue that the commonly used random replication does not take into account erasure coding in its design, thereby raising both performance and availability issues in the subsequent encoding operation. We propose encoding-aware replication, which carefully places the replicas so as to (i) eliminate cross-rack downloads of data blocks during the encoding operation, (ii) preserve availability without data relocation after the encoding operation, and (iii) maintain load balancing across replicas as in random replication before the encoding operation. We conduct extensive HDFS-based testbed experiments and discrete-event simulations, and demonstrate the performance gains of encoding-aware replication over random replication.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data, which are dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f +1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3 f +1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

Storage density of the traditional cloud storage system is not big and the comprehensive storage efficiency is low. When the large amount of data increases dramatically, the storage efficiency of the system would be decreased seriously. Therefore, with the development of cloud storage technology, an application of Markov Decision Process in Scheduling Strategy on cloud storage (MDPSS) is proposed in this paper. MDPSS can predict periodically in stages by using the steady Markov decision process and select the optimal storage node easily. By scheduling the data to the optimal node in real time, MDPSS can enhance the flexibility of the data storage and reduce the storage cost. Simulation results show that MDPSS has the ability to predict accurately when the size of the data is different.

Cloud security has gained increasingly emphasis in the research community, with much focus primary concentrated on how to secure the operation system and virtual machine on which cloud system runs on. We take an alternative perspective to consider the problem of building a secure cloud storage service on top of a public cloud infrastructure where the service provider is not completely trusted by the customer. So, it is necessary to put cipher text into the public cloud. We describe an architecture based on Trusted Platform Module and the client of cloud storage system to help manage the symmetric keys used for encrypting data in the public cloud and the asymmetric keys used for encrypting symmetric keys. The key management mechanism includes how to store keys, how to backup keys, and how to share keys. Based on the HDFS (Hadoop Distributed File System), we put a way of key management into practice, and survey the benefits that such an infrastructure will provide to cloud users and providers, and we also survey the time cost it will bring to us.

With the emergence of public cloud storage platforms like Amazon, Microsoft and Google etc, individual applications and some enterprise storage are being increasingly deployed on Clouds. However, dynamic data sharing in public clouds face problems of low performance and lack of SLA guarantees. We propose a middleware called mCloud between the Cloud storage and clients to provide data sharing services with better performance and SLA satisfaction. Technologies including virtualization, chunking, and caching, are integrated into mCloud. Experiential results based on Amazon Web Services (AWS) have shown that mCloud is able to improve shared IO performance in term of aggregate IO throughput and help provide better SLAs for cloud storage.

The public cloud storage auditing with deduplication has been studied to assure the data integrity and improve the storage efficiency for cloud storage in recent years. The cloud, however, has to store the link between the file and its data owners to support the valid data downloading in previous schemes. From this file-owner link, the cloud server can identify which users own the same file. It might expose the sensitive relationship among data owners of this multi-owners file, which seriously harms the data owners’ privacy. To address this problem, we propose an identity-protected secure auditing and deduplicating data scheme in this paper. In the proposed scheme, the cloud cannot learn any useful information on the relationship of data owners. Different from existing schemes, the cloud does not need to store the file-owner link for supporting valid data downloading. Instead, when the user downloads the file, he only needs to anonymously submit a credential to the cloud, and can download the file only if this credential is valid. Except this main contribution, our scheme has the following advantages over existing schemes. First, the proposed scheme achieves the constant storage, that is, the storage space is fully independent of the number of the data owners possessing the same file. Second, the proposed scheme achieves the constant computation. Only the first uploader needs to generate the authenticator for each file block, while subsequent owners do not need to generate it any longer. As a result, our scheme greatly reduces the storage overhead of the cloud and the computation overhead of data owners. The security analysis and experimental results show that our scheme is secure and efficient.

In the current digital era, people's need for data storage media that is practical and can be accessed at any time is increasing. This research aims to design and implement a practical cloud-based data storage system using a Raspberry Pi 4 Model B device using the Samba and OpenVPN applications. The system focuses on storing users' data (private cloud), which allows users to directly access files and data via a storage server. The method used in this research includes a literature review to support system development. Testing was carried out to evaluate the security of the system being built by comparing access to private cloud server services before and after using the OpenVPN application. Test results show that using the OpenVPN application increases the security of data exchange, with good encryption in communications between client and server. The resulting system runs according to the initial design and can function as a secure private cloud system. This research can contribute to the development of efficient and secure data storage solutions, as well as show the potential for using the Raspberry Pi as an energy and cost-saving personal cloud server device.

With the rapid developments occurring in cloud computing and services, there has been a growing trend to use the cloud for large-scale data storage. This has raised the important security issue of how to control and prevent unauthorized access to data stored in the cloud. One well known access control model is the role-based access control (RBAC), which provides flexible controls and management by having two mappings, users to roles and roles to privileges on data objects. In this paper, we propose a role-based encryption (RBE) scheme that integrates the cryptographic techniques with RBAC. Our RBE scheme allows RBAC policies to be enforced for the encrypted data stored in public clouds. Based on the proposed scheme, we present a secure RBE-based hybrid cloud storage architecture that allows an organization to store data securely in a public cloud, while maintaining the sensitive information related to the organization's structure in a private cloud. We describe a practical implementation of the proposed RBE-based architecture and discuss the performance results. We demonstrate that users only need to keep a single key for decryption, and system operations are efficient regardless of the complexity of the role hierarchy and user membership in the system.

The primary objective of this paper is to propose a security scheme for protecting the data to be stored in the public cloud and to reduce the computational power. Recently the usage of cloud storage increases due to its efficiency, reliability, and cost-effective usage by small and medium scale companies. The small and medium scale companies will not have enough infrastructures to store their data completely in their place. So, they will go for data outsourcing which helps them to store their data effectively in the cloud where the data security issue arise. In this paper, we have proposed a security scheme for storing data in the cloud storage. Our proposed method of security classifies the data into three group namely low sensitive data, medium sensitive data, and high sensitive data based on the role and responsibility of the user. It applies the various level of encryption and decrypting based on the sensitive level of the data. This method of security saves the computational power than the legacy methods that treats every data equally. Also, the simulation result shows that the proposed method offers better security level for high sensitive data than the existing legacy methods. This classification based encryption helps in reducing the computational load of the cloud environment.

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting deduplication and enabling users to perform search operations over encrypted data. In addition, it supports both multi-keyword and negative keyword searches. We formulate the security notions of MLSE and prove our scheme satisfies all the security requirements. Moreover, we provide an interesting extension of our construction to support Proof of Storage (PoS). Compared with the existing solutions, MLSE achieves better functionalities and efficiency, and hence enables more versatile and efficient cloud storage service.

This survey paper presents a comprehensive overview of AI-based traffic detection and autonomous signal operation systems, focusing on the integration of advanced technologies such as cameras, Raspberry Pi, and image processing algorithms, particularly YOLO (You Only Look Once). With the rapid urbanization and the increasing number of vehicles, efficient traffic management has become crucial for improving road safety and reducing congestion. Our survey examines various methodologies employed in traffic detection, including real-time monitoring and classification of vehicles, pedestrians, and cyclists. We analyze the effectiveness of using Raspberry Pi as a low-cost computing platform in conjunction with high-resolution cameras for image acquisition and processing. The paper highlights the performance of YOLO in detecting traffic objects in diverse environments, showcasing its speed and accuracy in real-time applications. Furthermore, we discuss the challenges faced in deploying these systems in real-world scenarios, including environmental variability, data privacy concerns, and the need for robust infrastructure. Finally, we propose future directions for research, emphasizing the importance of integrating machine learning with IOT solutions to create smarter, more responsive traffic management systems. This survey aims to provide insights for researchers and practitioners in developing sustainable traffic solutions leveraging AI technologies.

Cross-user deduplication is an effective technique to improve the efficiency of cloud storage by blocking redundant uploading of duplicate data. However, the deterministic response of this technique actually creates a side channel for attackers, which is able to be utilized to infer existence privacy of the target data in cloud storage. Because of the difficulty in achieving complete obfuscation in deduplication response, such kind of attack still cannot be well resisted, thus is becoming a big obstacle in using this technique. To deal with this challenge, we propose a cross-user generalized deduplication scheme for cloud storage, which takes the lead to achieve security against side channel attack by template extraction strategy. Specifically, we perform source-based deduplication to extracted templates and target-based strategy to the corresponding deviations. Because of the generalization strategy introduced, existence privacy of the target file cannot be inferred from deduplication response any more. The experimental results show that, with security against side channel attack guaranteed, the proposed scheme is more efficient in both communication and storage comparing with the state-of-the art.

Nowadays, more and more corporate and private users outsource their data to cloud service provider. It is provide lots of services to the users. Due to the large amount of data stored by multiple users on cloud one critical problem must be faced by users. In this large amount of data there are so many copies of repeated data. Data duplication is compress repeated data. We can use this technique To make data management scalable and mitigate the increased amount of data. In the proposed system, we are going to used two cloud server, public cloud server and private cloud server. Public cloud server store data (hash, encrypted data) and compare hash which is generated by private cloud. After comparison hash is stored in the data base by private cloud. This proposed system used to mitigate the problem of data duplication. Based on this we can improve storage utilization and efficiency of cloud storage.