Public Cloud Research Articles

An Approach to Managing Public Services that uses Big Data and Cloud Computing

Abstract: Governmental regulations have accelerated the development of information technology and resource grouping. One area of "digital gold" that is extremely practical and scientific is large-scale data mining. This is not simply a contemporary issue; it also calls for the rapid expansion of public service. As a result, it is possible to fully realise the advantages of information, improve service effectiveness, and close the service gap. Due to its speedy accounting, low memory requirements, and effective resource allocation, cloud computing has grown in popularity. Big data was therefore used in this work to analyse the current state of public cloud services, and fuzzy evaluation was used to analyse accurate monitoring. Eventually, the structure of the public service was enhanced, and a strategy for the precise organisation of the complete big data-driven public service process was created. According to a fuzzy comprehensive assessment, the design value and data entropy of public cloud services were increasing exponentially; the average design value of the accurate organisation for cloud-based government services was around 4.35, and the average significance of data entropy was around 0.98. Furthermore, big data-driven precision organisation of social public social welfare outperformed traditional precision monitoring of cloud-based services for the public, with results that suggested effects were 7% higher and overall capacity was 9% higher than the compared techniques.

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract: Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. First, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Second, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.

Data security crisis in universities: identification of key factors affecting data breach incidents

The extremely complex and dynamic digital environments of universities make them highly vulnerable to the risk of data breaches. This study empirically investigated the factors influencing data breach risks in the context of higher education, according to crime opportunity theory and routine activity theory. The data consisted of university samples from China and were collected mainly from the Chinese Education Industry Vulnerability Reporting Platform. After applying Poisson regression for the estimation, increased public disclosure of vulnerabilities was found to escalate the frequency of data breaches, whereas cross-border data flow decreased the number of data breaches. Furthermore, the mechanism by which academic strength affects data breaches was examined through the two mediators of cross-border data flow and vulnerability disclosure. In addition, cloud adoption reduced data breaches, and public clouds were determined to be relatively more secure than private clouds. Cloud adoption also acted as a moderator between the negative impact of vulnerabilities and the positive impact of cross-border data flow on data breaches. The estimation and robustness findings revealed the underlying mechanisms that impacted university data security, clarifying the understanding of data breaches and suggesting practical implications for universities and other institutes to improve information security. The findings of this study provide insights and directions for future research.

Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.

A Cryptographic Security Mechanism for Dynamic Groups for Public Cloud Environments

Cloud computing has emerged as a significant technology domain, primarily due to the emergence of big data, machine learning, and quantum computing applications. While earlier, cloud computing services were focused mainly on providing storage and some infrastructures/platforms for applications, the need to advance computational power analysis of massive datasets. It has made cloud computing almost inevitable from most client-based applications, mobile applications, or web applications. The allied challenge to protect data shared from and to cloud-based platforms has cropped up with the necessity to access public clouds. While conventional cryptographic algorithms have been used for securing and authenticating cloud data, advancements in cryptanalysis and access to faster computation have led to possible threats to the traditional security of cloud mechanisms. This has led to extensive research in homomorphic encryption pertaining to cloud security. In this paper, a security mechanism is designed targeted towards dynamic groups using public clouds. Cloud security mechanisms generally face a significant challenge in terms of overhead, throughput, and execution time to encrypt data from dynamic groups with frequent member addition and removal. A two-stage homomorphic encryption process is proposed for data security in this paper. The performance of the proposed system is evaluated in terms of the salient cryptographic metrics, which are the avalanche effect, throughput, and execution time. A comparative analysis with conventional cryptographic algorithms shows that the proposed system outperforms them regarding the cryptographic performance metrics.

Smash: Flexible, Fast, and Resource-efficient Placement and Lookup of Distributed Storage

Large-scale distributed storage systems, such as object stores, usually apply hashing-based placement and lookup methods to achieve scalability and resource efficiency. However, when object locations are determined by hash values, placement becomes inflexible, failing to optimize or satisfy application requirements such as load balance, failure tolerance, parallelism, and network/system performance. This work presents a novel solution to achieve the best of two worlds: flexibility while maintaining cost-effectiveness and scalability. The proposed method Smash is an object placement and lookup method that achieves full placement flexibility, balanced load, low resource cost, and short latency. Smash utilizes a recent space-efficient data structure and applies it to object-location lookups. We implement Smash as a prototype system and evaluate it in a public cloud. The analysis and experimental results show that Smash achieves full placement flexibility, fast storage operations, fast recovery from node dynamics, and lower DRAM cost (<60%) compared to existing hash-based solutions such as Ceph and MapX.

Accelerated Verifiable Fair Digital Exchange

A Fair Digital Exchange is defined as either all or none of the participants achieving a (predetermined) desirable outcome. This work addresses third party mediated systems for digital content where mutually unknown, and hence non-trusting, buyers, sellers and the mediator (third party) take part in an exchange protocol. We address the lack of guaranteed fairness, as defined above, in the existing platforms for this setting. We present TEDX, a decentralized solution for guaranteed three party fair exchange of digital goods with scalability and support for incremental deployment over the existing (non-fair) platforms. TEDX combines carefully crafted message exchanges with incentive schemes designed to deter malicious behavior. TEDX also leverages ideas from blockchain anchored state-channels to provide trusted execution while minimizing the operational overheads of blockchain. We present the design and a security analysis of TEDX to validate the claimed fairness properties. We also present the details of a prototype implementation of TEDX leveraging Hyperledger Fabric and performance evaluation of the same on a realistic testbed spanning five public cloud zones. Our results indicate that TEDX adds only a minimal overhead of 16% while being 46x faster than a naive blockchain solution, thereby demonstrating that TEDX is scalable.

A biobjective model for resource provisioning in multi-cloud environments with capacity constraints

Private and public clouds are good means for getting on-demand intensive computing resources. In such a context, selecting the most appropriate clouds and virtual machines (VMs) is a complex task. From the user’s point of view, the challenge consists in efficiently managing cloud resources while integrating prices and performance criteria. This paper focuses on the problem of selecting the appropriate clouds and VMs to run bags-of-tasks (BoT): big sets of identical and independent tasks. More precisely, we define new mathematical optimization models to deal with the time of use of each VMs and to jointly integrate the execution makespan and the cost into the objective function through a bi-objective problem. In order to provide trade-off solutions to the problem, we propose a lexicographic approach. In addition, we introduce, in two different ways, capacity constraints or bounds on the number of VMs available in the clouds. A global limit on the number of VMs or resource constraints at each time period can be defined. Computational experiments are performed on a synthetic dataset. Sensitivity analysis highlights the effect of the resource limits on the minimum makespan, the effect of the deadline in the total operation cost, the impact of considering instantaneous capacity constraints instead of a global limit and the trade-off between the cost and the execution makespan.

მონაცემთა ცენტრის მომავალი, მისი დადებითი და უარყოფითი მხარეები

Why are more and more large and mid-sized organizations now considering data center outsourcing? Are the pros of IT outsourcing overblown? Or do organizations want to embrace the trend for the financial and operational advantages that data center management outsourcing offers? Unfortunately, a simple yes or no will not be enough to answer the above questions. According to a recent survey of Fortune 500 companies by Forbes, most IT companies now prefer IT outsourcing over in-house data center operations due to the advantages that data center outsourcing offers. This includes minimal operational costs, better utilization of infrastructure, access to servers and compute/storage on demand, etc. Before you decide to outsource your data center, carefully research your options, analyze your business prospects, and conduct a careful examination of your assets, budgets, and requirements. Carefully evaluating the pros and cons of data center outsourcing and how it might affect your business is something you should consider before signing an outsourcing contract. Data centers are critical to today's business scene. Although their demands have always been on an upward trajectory, the situation created by the COVID-19 pandemic has pushed it further. Like all other segments in and around IT, data center technologies are rapidly changing to achieve a highly optimized and efficient system. Therefore, it is very important for you as a business owner to stay up to date with each of these technological changes and trends. After all, they are going to create the future of data centers. Advances in technology will help the overall performance of the data center. Predictive analytics tools can help security and real-time monitoring teams spot potential problems early and fix them before they impact operational performance. Data center AI can highlight areas that could work more efficiently, such as cooling or power consumption. Thus, data center composability means creating resources that can work with many different systems. Instead of dedicated or narrow-use resources providing only one service, data centers should use resource pools that can contribute to the deployment of different types of workloads, such as storage, networking, or server equipment. Likewise, the implementation of composite resources helps to avoid locked capacity in the data center, thereby improving efficiency and throughput. Above all, digital transformation will enable data centers to deliver additional value to customers. Edge computing will help providers run low-latency workloads with end users, while software virtualization will create more budget-friendly options for multiple workloads. As private and public clouds combine to create more flexible and robust hybrid cloud deployments, companies will leverage technology to do more with less.

Cracking-Like Join for Trusted Execution Environments

Data processing on non-trusted infrastructures, such as the public cloud, has become increasingly popular, despite posing risks to data privacy. However, the existing cloud DBMSs either lack sufficient privacy guarantees or underperform. In this paper, we address both challenges (privacy and efficiency) by proposing CrkJoin, a join algorithm that leverages Trusted Execution Environments (TEE). We adapted CrkJoin to the architecture of TEEs to achieve significant improvements in latency of up to three orders of magnitude over baselines in a multi-tenant scenario. Moreover, CrkJoin offers at least 2.9x higher throughput than the state-of-the-art algorithms. Our research is unique in that it focuses on both privacy and efficiency concerns, which has not been adequately addressed in previous studies. Our findings suggest that CrkJoin makes joining in TEEs practical, and it lays a foundation towards a truly private and efficient cloud DBMS.

Discrete Hyperchaotic S-Box Generation for Selective Video Frames Encryption

Cloud computing has become a significant technology with rapid popularization offering many services with highly effective computation and large-scale storage solutions. As multimedia files are growing explosively, securely storing them for authorized access is a major task. Concentrating on video file storage in a public cloud environment, the security and privacy of those outsourced videos are the major concerns. A selective frame encryption approach is proposed to encrypt the semantic elements of the selected frames of High Efficiency Video Coding (HEVC) compressed videos by storing them in the cloud. The semantic elements of HEVC videos are extracted and encrypted by a modified Advanced Encryption Standard (AES) operation in CFB mode with a Substitution-Box (S-Box). A dynamic, nonlinear 2D discrete hyperchaotic mapping-based S-box generation method is proposed to enhance security by providing good confusion, diffusion, and scrambling. The performance analysis of generated S-box and experimental results show that the proposed selective encryption highly resists attacks and encrypts with reduced time complexity.

A Proveable Contextual Retrieval Strategy in the Public Cloud Using Optimal Matching over Secured Data

Abstract: Semantic searching over encrypted data is a crucial task for secure information retrieval in public cloud. It aims to provide retrieval service to arbitrary words so that queries and search results are flexible. In existing semantic searching schemes, the verifiable searching does not be supported since it is dependent on the forecasted results from predefined keywords to verify the search results from cloud, and the queries are expanded on plaintext and the exact matching is performed by the extended semantically words with predefined keywords, which limits their accuracy. In this paper, we propose a secure verifiable semantic searching scheme. For semantic optimal matching on ciphertext, we formulate word transportation (WT) problem to calculate the minimum word transportation cost (MWTC) as the similarity between queries and documents, and propose a secure transformation to transform WT problems into random linear programming (LP) problems to obtain the encrypted MWTC. For verifiability, we explore the duality theorem of LP to design a verification mechanism using the intermediate data produced in matching process to verify the correctness of search results. Security analysis demonstrates that our scheme can guarantee verifiability and confidentiality. Experimental results on two datasets show our scheme has higher accuracy than other schemes.

An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

The hybrid cloud has attracted more and more attention from various fields by combining the benefits of both private and public clouds. Task scheduling is still a challenging open issue to optimize user satisfaction and resource efficiency for providing services by a hybrid cloud. Thus, in this paper, we focus on the task scheduling problem with deadline and security constraints in hybrid clouds. We formulate the problem into mixed-integer non-linear programming, and propose a polynomial time algorithm by integrating swarm intelligence into the genetic algorithm, which is named SPGA. Specifically, SPGA uses the self and social cognition exploited by particle swarm optimization in the population evolution of GA. In each evolutionary iteration, SPGA performs the mutation operator on an individual with not only another individual, as in GA, but also the individual’s personal best code and the global best code. Extensive experiments are conducted for evaluating the performance of SPGA, and the results show that SPGA achieves up to a 53.2% higher accepted ratio and 37.2% higher resource utilization, on average, compared with 12 other scheduling algorithms.

System Level Benchmarking of Public Clouds

It is important for cloud users to be able to evaluate and compare different cloud services to achieve high performance and maximize cost savings. To that end, this research benchmarked Amazon web services elastic compute cloud and Rackspace cloud infrastructure and compared the results for the two public cloud providers. The intent of the study was to determine how these selected providers perform with regards to system parameter usage and hence three system-level benchmarks: STREAM, IOR, and NPB-EP were run on different configurations to provide an insight to the cloud users in selection of provider on VM clusters of 1,2,4,6, and 8 nodes. The clusters were created with similar virtual machines from both providers. The benchmarks examined bandwidth, I/O and CPU performance. A comparison of results for the two providers is presented graphically and T-test applied to determine if differences are significant. Observations were taken at multiple times at different time periods on weekdays and weekends to examine variance of cloud performance.

Secure Role Based Access Control Data Sharing Approach and Cloud Environment

the primary objective of cloud storage is to maintain data integrity, which involves implementing measures to prevent unauthorized access and ensuring that data can be regenerated if mishandled. To achieve this, a proxy server will be assigned the task of protecting and restoring data in case of unauthorized modifications. Users' data will be stored in both public and private areas of the cloud, with only public data being accessible to users and private data being kept more secure. Cloud storage offers users various redundancy configurations to balance performance and fault tolerance, with data availability being critical in distributed storage systems, especially when node failures are common in real-life scenarios. In this research, a proposed aes 128 encryption algorithm and role-based access control (rbac) scheme are explored to provide secure data storage and sharing, as well as a secure user access policy. Additionally, a backup server approach is used as a proxy storage server for ad hoc data recovery for all distributed data servers. The experiment's analysis is proposed in both public and private cloud environments, utilizing keywords such as rbac, elgamal encryption scheme, proxy key generation, advanced encryption standard (aes), and more

Threshold Cryptography-Based Secure Vehicle-to-Everything (V2X) Communication in 5G-Enabled Intelligent Transportation Systems

Implementing 5G-enabled Vehicle-to-Everything (V2X) intelligent transportation systems presents a promising opportunity to enhance road safety and traffic flow while facilitating the integration of artificial intelligence (AI) based solutions. Yet, security and privacy concerns pose significant challenges that must be addressed. Therefore, researchers have focused on improving the security and integrity of vehicle data sharing, with a particular emphasis on V2X application layer security and privacy requirements. This is crucial given that V2X networks can consist of vehicles manufactured by different companies and registered in various jurisdictions, which may only be within communication range for a few seconds. Thus, it is necessary to establish a trusting relationship between vehicles quickly. The article proposes a threshold cryptography-based key exchange protocol that meets the key requirements for V2X data sharing and privacy, including the rapid establishment of trust, the maintenance of vehicle anonymity, and the provision of secure messages. To evaluate the feasibility and performance of the proposed protocol, a tailored testbed that leverages the NS-3 network simulator, a commercial 5G network, and public cloud infrastructure is used. Overall, the proposed protocol provides a potential solution for addressing security and privacy concerns in V2X networks, which is essential for successfully implementing and adopting this technology.

An Efficient Privacy - Aware Authentication Framework for Mobile Cloud Computing

The approach of distributed computing, information proprietors are inspired to re-appropriate their intricate information the board frameworks from neighbourhood locales to business public cloud for extraordinary adaptability and financial investment funds. Yet, for safeguarding information security, delicate information must be scrambled prior to re-appropriating, which obsoletes customary information use in view of plaintext catchphrase search. In this way, empowering an encoded cloud information search administration is of fundamental significance. Taking into account the enormous number of information clients and reports in cloud, it is pivotal for the pursuit administration to permit multi-watchword question and give result similitude positioning to meet the compelling information recovery need. Related works on accessible encryption centre around single catchphrase search or Boolean watchword search, and seldom separate the indexed lists. In this paper, interestingly, we characterize and tackle the difficult issue of protection safeguarding multi-watchword positioned metaphysics catchphrase planning and search over encoded cloud information (EARM), and lay out a bunch of severe protection prerequisites for such a solid cloud information use framework to turn into a reality. Content-based distribute/buy in gives an inexactly coupled and expressive type of correspondence for enormous scope circulated frameworks. Secrecy is difficult for distribute/buy in middleware conveyed over various regulatory areas. This component that lessens the expense of encoded coordinating, as a profiteering administrator utilizing Blossom channels and straightforward randomization procedures. Propose regulation obscurity procedures and give a thorough security examination of the data spilled by Blossom channels for this situation. Among different multi-watchword semantics, we pick the effective guideline of "Improved Affiliation Rule Mining coordinate coordinating", i.e., whatever number matches as could reasonably be expected, to catch the closeness between search inquiry and information records, and further use "inward item likeness" to formalize such standard for comparability estimation quantitatively. We initially propose an essential EARM conspire utilizing secure inward item calculation, and afterward fundamentally further develop it to meet different protection prerequisites in two degrees of danger models.

A world with Cloud Computing

Cloud computing is a type of computing that utilizes a network on the internet rather than a local server or a personal computer to store, manage, and process data,. It is a model of service delivery that allows businesses to access technology-enabled services over the internet. Cloud computing provides a range of benefits to users, including scalability, cost savings, increased collaboration and productivity, and improved disaster recovery and business continuity. Moreover, cloud computing offers businesses the opportunity to access enterprise-level applications and services without the need to invest in costly hardware and software. This abstract outlines the basics of cloud computing, its benefits, and the potential for businesses to leverage it for their own success. Cloud computing is a model of delivering on- demand computing resources, including servers, storage, and applications, over the internet. In this model, users can access these resources from anywhere and at any time without needing to manage or own the underlying infrastructure. Cloud computing offers several benefits, including scalability, cost efficiency, and flexibility. It enables businesses to easily scale their operations up or down based on changing demands, pay only for the resources they use, and access a range of services without needing to invest in their own hardware and software. Cloud computing has become increasingly popular in recent years, with public cloud providers like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominating the market. As more businesses move their operations to the cloud, the need for skilled cloud computing professionals is also increasing. KEYWORDS Cloud computing, services, businesses,storage ,data,storage,

A PHP Implementation of a Smart Tourism Management System under A Cloud Computing Platform

This paper collects and analyzes the use of cloud computing technology in the tourism industry at home and abroad, combines its advantages and disadvantages, uses PHP language for web design, service modeling and data modeling, and then proposes a tourism cloud service architecture. According to the characteristics of tourism business, the system construction of the tourism cloud platform is divided into three parts: private tourism cloud, tourism exclusive cloud, and tourism public cloud. In addition, specific and detailed planning and design of the tourism private cloud platform have been carried out, and the efficiency has increased by 7.12%. And in response to the rapid growth of tourism data, this article also proposes a recommendation algorithm based on tourist and scenic spot information.

MTCSNet: Mean Teachers Cross-Supervision Network for Semi-Supervised Cloud Detection

Cloud detection methods based on deep learning depend on large and reliable training datasets to achieve high detection accuracy. There will be a significant impact on their performance, however when the training data are insufficient or when the label quality is low. Thus, to alleviate this problem, a semi-supervised cloud detection method, named the mean teacher cross-supervision cloud detection network (MTCSNet) is proposed. This method enforces both consistency and accuracy on two cloud detection student network branches, which are perturbed with different initializations, for the same input image. For each of the two student branches, the respective teacher branches, used to generate high-quality pseudo labels, are constructed using an exponential moving average method (EMA). A pseudo one-hot label, produced by one teacher network branch, supervises the other student network branch with the standard cross-entropy loss, and vice versa. To incorporate additional prior information into the model, the presented method uses near-infrared bands instead of red bands as model inputs and injects strong data augmentations on unlabeled images fed into the student model. This induces the model to learn richer representations and ensure consistency constraints on the predictions of the same unlabeled image across different batches. To attain a more refined equilibrium between the supervised and semi-supervised loss in the training process, the proposed cloud detection network learns the optimal weights based on homoscedastic uncertainty, thus effectively exploiting the advantages of semi-supervised tasks and elevating the overall performance. Experiments on the SPARCS and GF1-WHU public cloud detection datasets show that the proposed method outperforms several state-of-the-art semi-supervised algorithms when only a limited number of labeled samples are available.