Public Cloud Research Articles

Blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection

The cloud computing technology has emerged, developed, and matured in recent years, consequently commercializing remote outsourcing storage services. An increasing number of companies and individuals have chosen the cloud to store their data. However, accidents, such as cloud server downtime, cloud data loss, and accidental deletion, are serious issues for some applications that need to run around the clock. For some mission and business-critical applications, the continuous availability of outsourcing storage services is also necessary to protect users' outsourced data during downtime. Nevertheless, ensuring the continuous availability of data in public cloud data integrity auditing protocols leads to data privacy issues because auditors can obtain the data content of users by a sufficient number of storage proofs. Therefore, protecting data privacy is a burning issue. In addition, existing data integrity auditing schemes that rely on semi-trusted third-party auditors have several security problems, including single points of failure and performance bottlenecks. To deal with these issues, we propose herein a blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection. We realize a concrete construction by using a verifiable delay function with high efficiency and proof of retrievability, and prove the security of the proposal in a random oracle model. The proposed construction supports dynamic updates for the outsourced data. We also design smart contracts to ensure fairness among the parties involved. Finally, we implement the protocols, and the experimental results demonstrate the efficiency of the proposed protocol.

Flexible Wildcard Searchable Encryption System

Searchable Encryption is a cryptographic technique which allows search of data in an encrypted format. This technique provides user data confidentiality insurance for public cloud storage service as well as allows multiple keyword search over encrypted data. Previously existing systems provide exact or fuzzy keyword search which corrects spelling errors only and their features are limited. In this paper, we propose a system known as adaptable wildcard searchable encryption system which allows mu.

A Secure Interoperable API Wrapper Tunnel for Integration of GIS-Based ICS-IIoT and Digital Twins in Industry 4.0 Clouds

Industry 4.0 is the most disruptive revolution engulfing all the digital transformation from sensors nodes to cloud applications. A sustainable and secure Industry 4.0 ecosystem is a core challenge for stakeholders and state agencies addressed in this work by layered API tunnel interoperability instead of conventional cybersecurity methods and techniques. In this work, an interoperable application programmer interface (API) wrapper is being proposed to manage heterogeneous cloud architectures using Eucalyptus, Apache CloudStack, and OpenNebula. A comprehensive design and implementation plan from things layer to user interface has been accomplished in this work. Infrastructure as a Service (IaaS) is being automated using Ansible from Web Servers Layers through Common Gateway Interface (CGI) scripting. The Web Servers are being managed using WebSockets by micro–Web Servers Gateway Interface (uWSGI) from cloudstack managers (CSM) API. The Platform as a Service (PaaS) for big data, web servers, and machine learning platforms is accomplished using uWSGI from CSM. The public and private clouds are being managed using individual APIs. The wrapper, proposed in this work enables the big challenge of Industry 4.0 for realizing interoperability, scalability, heterogeneity, and automated cloud integration by functioning at the Software as a Service (SaaS) Layer.

Reducing response latency of composite functions-as-a-service through scheduling

In Function-as-a-Service (FaaS) clouds, customers deploy to cloud individual functions, in contrast to complete virtual machines (IaaS) or Linux containers (PaaS). FaaS offerings are available in the largest public clouds (Amazon Lambda, Google Cloud Functions, Azure Serverless); there are also popular open-source implementations (Apache OpenWhisk) with commercial offerings (Adobe I/O Runtime, IBM Cloud Functions). A recent addition to FaaS is the ability to compose functions: a function may call another functions, which, in turn, may call yet another function — forming a directed acyclic graph (DAG) of invocations. From the perspective of the infrastructure, a composed function is less opaque than a virtual machine or a container. We show that this additional information about the internal structure of the function enables the infrastructure provider to reduce the response latency. In particular, knowing the successors of a function in a DAG, the infrastructure can schedule these future invocations along with necessary preparation of environments.We model resource management in FaaS as a scheduling problem combining (1) sequencing of invocations; (2) deploying execution environments on machines; and (3) allocating invocations to deployed environments. For each aspect, we propose heuristics that employ FaaS-specific features. We explore their performance by simulation on a range of synthetic workloads and on workloads inspired by trace from existing system. Our results show that if the setup times are long compared to invocation times, algorithms that use information about the composition of functions consistently outperform greedy, myopic algorithms, leading to significant decrease in response latency.

A Comprehensive Cloud Security Model with Enhanced Key Management, Access Control and Data Anonymization Features

A disgusting problem in public cloud is to securely share data based on fine grained access control policies and unauthorized key management. Existing approaches to encrypt policies and data with different keys based on public key cryptosystem are Attribute Based Encryption and proxy re-encryption. The weakness behind approaches is: It cannot efficiently handle policy changes and also problem in user revocation and attribute identification. Even though it is so popular, when employed in cloud it generate high computational and storage cost. More importantly, image encryption is some out complex in case of public key cryptosystem. On the publication of sensitive dataset, it does not preserve privacy of an individual. A direct application of a symmetric key cryptosystem, where users are served based on the policies they satisfy and unique keys are generated by Data Owner (DO). Based on this idea, we formalize a new key management scheme, called Symmetric Chaos Based key Management (SCBKM), and then give a secure construction of a SCBKM scheme called AS-Chaos. The idea is to give some secrets to Key Manager (KM) based on the identity attributes they have and later allow them to derive actual symmetric keys based on their secrets. Using our SCBKM construct, we propose an efficient approach for fine-grained encryption-based access control for data stored in untrusted cloud storage.

Cost Effective Cloud Storage Interoperability Between Public Cloud Platforms

With recent advancement in technology, cloud storage became cheaper enabling organizations around the world to store more data on the cloud (texts, images, videos, databases etc.), whereas it’s for a backup, archiving or just storing data streams. New digital laws and regulation (eg. General Data Protection Regulation) require these organizations to change their way of processing or handling data, which results usually in a change of cloud providers or adoption of hybrid architecture or multi-cloud one. With the amount of data stored increasing year after year, it becomes difficult for these organizations to change cloud platforms or cloud provider and migrate their data without thinking about the technical complexity, the time and the huge cost it may incur. This article discusses the data migration and interoperability issues between cloud platforms; the proposed approach provides a simple cost-effective migration that would help organizations save time and money in this process based on a hybrid ontology approach for the brokerage of data transfers.. Keywords-Cloud Computing; Storage; Security; data; migration; cost optimization;

Bulut bilişimde sunucusuz mimariler ile coğrafi bilgi teknolojilerinin kullanımı üzerine bir inceleme

From the perspective of resource management, cloud computing falls into three categories, namely, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each model can be clarified in the responsibility share of infrastructure and implementation management on the user and provider. Nowadays, as an addition to these models, the notion of Function as a Service (FaaS) has appeared, based on serverless architecture, implying serverless services. With this model, in brief, a developed code is run scalable depending on a request or an event. The user is responsible only for the development and uploading of the code. This increasingly widespread model has been applied not only to run the code but also to services such as data storage and data analysis. Thus, the scope of the model has been expanded. This study deals with various Geospatial Information Systems (GIS) from the viewpoint of their use on cloud computing with serverless architectures. In this context, various GIS applications were selected to deploy a public cloud computing provider’s services, and results were discussed. In this way, it is aimed to raise awareness by examining the use of services and software based on increasingly popular serverless architectures with different GIS applications. In addition, the interaction of trending technologies and computing paradigms in the cloud computing area such as GIS technologies, spatial intelligence, and big data, with serverless architectures are reviewed; and a contributory road map for the future is proposed.

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

The fact that cloud systems are under the increasing risks of cyber attacks has made the phenomenon of information security first a need and then a necessity for these systems. Distributed Denial of Service (DDoS) attacks can exploit, disrupt, change, prevent or damage cloud services. Accurate and timely detection and prevention of these attacks are very important in terms of ensuring information security. During the COVID-19 period, the increase in the use of information technologies and especially the internet has made cyber attacks a real concern. Deep learning (DL) has become widely used for the purpose of detecting and preventing cyber attacks to provide information security. In this study, a Long Short-Term Memory (LSTM) based system (LSTM-CLOUD) which was designed for the detection and prevention of DDoS attacks in a public cloud network environment was proposed. The design of the system is based on a signature-based attack detection approach. The LSTM-CLOUD has two modules defined in the study: detection and defense. The function of the first module of the system was determined as detecting the occurrence of DDoS attacks with the LSTM DL model developed in this study with an accuracy rate of 99.83% on the CICDDoS2019 data set. The function of the second module was determined as activating the defense mechanism to protect the cloud systems when attacks are detected. The comparison results showed that our LSTM model had a performance as good as those in the previous studies conducted with different DL algorithms on the same and different datasets. The results obtained show the effectiveness of the LSTM model developed in this study in detecting the occurrence of attacks.

An improved hybrid cloud workflow scheduling algorithm based on ant colony optimization

At present, with the enormous development of cloud-based applications, the demand of related enterprises for computing and a large number of storage resources is increasing day by day. Due to the characteristics of ultra-large scale and low cost, cloud computing has been gradually applied to complex workflow scheduling problems in various fields. The traditional scheduling algorithms have lot of limitations such as inefficient task management and unreasonable resource allocation. The workflow scheduling problem in the cloud computing environment has been proved to be an NP-complete problem. To solve these problems, this paper proposes an improved ant colony optimization workflow scheduling for workflow execution of tasks within a user-specified deadline. On this basis, a hybrid cloud deadline-constrained cost workflows scheduling algorithm under the hybrid cloud is proposed, which will prioritize scheduling execution in the private cloud, and use the public cloud to schedule part of the workflow when the task execution time exceeds the task deadline constraint. The simulation results show that the proposed workflow scheduling algorithm reduces the cost and attain faster execution time.

An optimized novel public cloud system to secure the medical record from third parties

AbstractNowadays, modern technologies are growing rapidly by advancing their application day by day. Moreover, the Internet of Things (IoT) is well utilized in many applications for sensing and information gathering purposes. Considering this, medical applications have used the advances of IoT facilities in sensing the disease rate in humans. After sensing, the sensed data is stored in the cloud system, so securing the cloud system is the more required task to maintain the privacy of the patient's data. But, in front of malicious activities, securing the data is not easy. So, the present research has proposed a novel African buffalo‐based International Data Encryption (ABIDE) public cloud system to secure the medical database during the data transmission. The developed technique is utilized in a public cloud system. The main objective of this research is to secure medical data during transmission. Moreover, the implementation of the proposed ABIDE algorithm is done in the MATLAB environment. Consequently, the performance of the developed technique is compared with existing methods in terms of resource usage, encryption time, decryption time and so forth. Therefore, the proposed model has attained the finest confidential rate of 98.98%, throughput 97.86% and less processing time of 4 ms.

A Cloud-Fog Continuum Computing Architecture for Cyber-Manufacturing Systems

Cyber-Manufacturing System (CMS) is a vision for the factory of the future, where physical components and processes are seamlessly integrated with computing processes to form highly adaptive and responsive manufacturing operations. In CMS, manufacturing resources and capabilities are digitized and shared with users and stakeholders through a local area network (LAN) and the Internet. CMS aims to utilize the manufacturing data obtained during all product lifecycle phases to provide agile and scalable manufacturing solutions. Currently, a centralized cloud-based computing environment supported by the distributed Internet of Things (IoT) devices network is used to enable the typical functionalities—such as manufacturing resource sharing and large-scale manufacturing collaborations. However, facing the explosion of manufacturing data from factory floors, cloud-based computing solutions show limitations in providing low-latency services, performing real-time state analysis, configuring the machines, and controlling other executors in the physical manufacturing end. Furthermore, private production data and technical details cannot be appropriately masked in the public cloud platform. In this research, a Cloud-Fog Continuum Computing Architecture is introduced to better utilize and govern the manufacturing data for manufacturing enterprise stakeholders and customers in CMS. A Hadoop-Raspberry Pi computing system is presented as a proof-of-concept of the proposed continuum computing mechanism to provide machining services in CMS.

Greening the Cloud: A Load Balancing Mechanism to Optimize Cloud Computing Networks

ABSTRACT Approximately 55 percent of the world’s 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users’ needs on demand. Greening cloud computing technology has become an important aspect of an organization’s design and use of cloud computing. One aspect of greening the cloud is through efficiently using cloud-based resources. In this study, we focus on the resource allocation applications of cloud computing technologies to green an organizations cloud. We design a pricing and allocation mechanism for a private cloud computing service that allows the firm to effectively load balance their cloud computing resources. Our optimal pricing mechanism is a dynamic pricing model, which maximizes the net value of users in a private cloud computing service. Additionally, we create a job allocation algorithm based on our dynamic pricing model to load balance the cloud. We show that balancing the job allocation such that the number of jobs at individual resource servers as close to equal as possible is optimal. Furthermore, simulations were run on the allocation mechanism to examine the effects on the cloud resources while gaining insight to effectively distribution resources to public clouds. Our model can help organizations to efficiently distribute their cloud-based resources, which allows for a greener cloud computing system.

Review of Advanced Techniques for Data Security Technology and Applications

COVID-19 continues to dominate world news because of its severe damage to human health, which may lead to death. It has been forcing people to handle their work, studies, and examinations in the public cloud. In this situation, data security should be ensured to prevent data from being hacked or altered. One of the most promising fields in data security is DNA cryptography, which refers to using DNA molecules in storing, hiding, and transmitting the information. This systematic review discusses some cryptographic techniques and their progress.

Secure and Authenticated Data Access and Sharing Model for Smart Wearable Systems

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.

Public Sector Cloud Computing Adoption and Utilization During Covid-19: An Agenda for Research and Practice

Cloud computing became a pivotal crisis response tool for many public sector organizations (PSOs) during the COVID-19 pandemic, sustaining public service delivery and public sector operations during times of extraordinary global turmoil. The technology’s inherent strengths of flexibility, innovation, resilience, and collaboration prompted PSOs to aggressively pursue both the initial adoption of cloud and the expansion of already-existing cloud computing capabilities. Despite the importance of the emerging topic of crisisdriven public sector cloud adoption for future crisis response efforts and the post-crisis transition to regular public sector operations, academic literature and empirics are sparse and present a distinct knowledge gap. This article assesses crisis-driven public sector cloud computing adoption and expanded utilization and provides recommendations for the advancement of research and practice, supporting future research, collaboration, and evidence-based cloud computing implementation and utilization.

A Higher-Level Security Scheme for Key Access ON Cloud Computing

Abstract: In this work, we employ a key access control management scheme which effortlessly transitions any organization-like security policy to state of the art cloud level security. Offering a very flexible, secure, and hierarchical key access mechanism for institutions that deal with mission-critical data. The scheme also minimizes concerns about moving critical data to the public cloud and ensures only the users with sufficient permission from equal or higher privileged members can access the key by the use of topological ordering of a directed graph which includes self-loop. The main overheads such as public and private storage needs are restricted to a level that is tolerable, and the derivation of key is computationally fast and efficient. From a security perspective, the proposed scheme would be resistant to collaboration attacks and would provide key in distinguishability security. Since the key isn’t stored anywhere so, the problem of a data breach is eliminated. Keywords: Cloud platform; plagiarism check; Shamir’s Secret Key, Cloud security, hierarchical, interpolation, key access, key assignment, secret sharing.

Flexible and Controllable Access Policy Update for Encrypted Data Sharing in the Cloud

Abstract As a promising service paradigm, cloud computing has attracted lots of enterprises and individuals to outsource big data to public cloud. To facilitate secure data using and sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. However, some serious challenges are still remaining toward achieving flexible and controllable access policy update in CP-ABE, which essentially impede the powerful access control ability of CP-ABE from long-term and large-scale deployment in real systems. In this work, we propose a novel scheme named policy updatable CP-ABE for encrypted data sharing scenes. The proposed scheme features the following achievements: (i) it supports fine-grained update algorithms with no restriction on update time; (ii) the cloud server can effectively verify update ciphertexts, so the integrity of original data would not be compromised intentionally or accidentally during the update and (iii) most operations of encryption and policy update are securely outsourced to cloud servers, leaving extremely low overheads for data owners and users. We formally define its security model and prove it is adaptively secure. Also, we implement the proposed scheme using the Charm framework. The experiment results demonstrate that it is efficient and practical.

Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data

The efficiency of a spatiotemporal data analysis algorithm decreases as the amount of data increases. Many clustering techniques have been proposed for data analysis applications. However, applying those techniques to spatiotemporal data clustering is still in its infancy. In this paper, we tackle the issue of clustering spatiotemporal data on public Cloud based on the distance between them. To increase the efficiency of spatiotemporal clustering, we have proposed a MapReduce-based framework for clustering. However, as spatiotemporal dataset contains sensitive information, directly outsourcing spatiotemporal data to Cloud servers will raise privacy concerns. To address the problem of privacy, we have proposed a privacy preserving clustering algorithm based on MapReduce for spatiotemporal data that can be efficiently outsourced for data processing on the Cloud servers. The proposed scheme allows the clustering operation to be performed directly on the encrypted spatiotemporal data by Cloud server. Extensive experimental evaluation with trajectory data shows that our scheme efficiently produces higher quality clustering results.

An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture

Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechanism for proper application mapping in order to produce maximum benefits in terms of application-level latency, platform energy consumption, and system throughput. Similarly, the neural-network (NN)-based artificial intelligence (AI) techniques for deep learning are gaining particular interest. These applications can be executed on a cloud-based system, but some of these applications have to be executed on private cloud to integrate the data privacy. Furthermore, the public cloud systems can also be made from these NoC platforms to have better application performance. Therefore, there is a need to optimally map these applications on existing NoC-based architectures. If the application is not properly mapped, then it can create a performance hazard that may lead to delay in calculations, increase in energy consumption, and decrease in the platform lifetime. Hence, the real-time applications requiring AI services can implement these algorithms in NoC-based architectures with better real-time performance. In this article, we propose a multilevel mapping of deep learning AI applications on the NoC architectures and show its results for the energy consumption, task distribution profile, latency, and throughput. The simulation is conducted using the OCTAVE, and the simulation results show that the performance of the proposed mapping technique is better than the direct mapping techniques.

Proposed Event Organizer Small Medium Business Enterprise Architecture in Pandemic Industry 4.0 Era

Covid-19 pandemic affects businesses around the world and the most impacted are small and medium event organizer business (SMB EO) that usually having social gathering event participated by a few hundred participants only. Digital transformation must be executed immediately to adapt to the conditions utilizing Industry 4.0 technology especially in recent pandemic era. This paper study the enterprise architecture design of SMB EO utilizing Industry 4.0 to survive in pandemic era and beyond. The enterprise architecture based on Togaf framework consists of business, application, information, and technology architecture is decribed in this paper. Small business doesn’t have the big capital on investing in technologies and during pandemic where certain technology is scarce may cause the business to be left behind. Choice of utilizing public social media and cloud providers according to the architecture is the key. Once the technology is adopted, business is digitalized then business can be ready to face future metaverse business model.