Sina Ahmadi
email: sina0@acm.org

Security and Privacy Challenges in Cloud-Based Data Warehousing: a Comprehensive Review

Keywords: data warehousing, security, privacy, cloud I

Nowadays, certain trends in technology have emerged, especially in cloud-based data warehousing. Organizations and associations use cloud-based data warehousing to store large amounts of data. However, this data warehousing type has many risks and challenges, such as privacy concerns. Some major security challenges are data breaches, malware attacks and data theft, which violates legal privacy frameworks, such as the Consumer Privacy Act. Certain measures like contractual agreements and data ownership can control these risks. The major object of this paper is to discuss the security and privacy challenges in cloud-based data warehousing used by private and government organizations. Some important challenges are complex cloud computing models, dynamic nature and interconnected ecosystems. The need for more resources is another major challenge for the companies which comes with the budgeting issues.

INTRODUCTION

Information technology has now become an important element in cloud-based data and warehousing. It has revolutionized the ways that different organizations and associations use to analyze vast datasets. Information technology has also brought certain challenges to accomplishing security and privacy matters. Due to this, it has become important to understand the benefits of cloud-based data storage and the risks of securing such sensitive information.

Cloud-based data or cloud computing plays a vital role in workflow management, providing many computing resources on demand to run data or compute critical applications. It also helps reduce costs and increase revenues while improving the quality of services. However, despite these advantages, cloudbased data is a huge area of concern that is creating restrictions and problems for organizations regarding security risks and malicious attacks. The complicated Infrastructure of cloud computing also creates risks, such as sharing stored data with other users or transferring sensitive data among other cloud components such as Data Centers. In some cases, the sensitive information is deduced from the cloud-based data and is leaked or sold to third parties by hackers. Therefore, cloud-based data warehousing has become a huge challenge for organizations to foster a secure and compliant data ecosystem.

II. CLOUD-BASED DATA WAREHOUSING OVERVIEW

A. Definition and Characteristics

Cloud-based data warehousing is a platform where the consumer can easily store a large amount of data and manage its platform and application for different purposes [START_REF] Nambiar | An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management[END_REF]. Some of the major examples are Google apps or IaaS-type services. There are two types of cloud-based warehousing: the private cloud and the hybrid cloud. The private cloud is the one that is handled by a single person or single organization and for a single business purpose.

On the other hand, a hybrid cloud system combines two or more infrastructures, whether public or community [START_REF] Talaat | Hybrid-cloud-based data processing for power system monitoring in smart grids[END_REF]. The cloud-based data is continuously growing in computing and has proven to be a major shift in the world of technology to improve scaling, agility, functionality, collaboration, and low-cost reduction. However, certain challenges related to cloud-based data have attracted the attention of certain service providers and researchers.

There are different types of cloud-based challenges or risks, which have been classified into two main categories: privacy and security. Both of these challenges affect the efficiency and reliability of cloud-based environments. The importance of cloud-based data warehousing in modern IT infrastructure cannot be pointed out, as it tackles critical difficulties while opening up unprecedented potential for enterprises across multiple industries [START_REF] Plašić | Enterprise Business Intelligence Approach With Cloud-Based Analytics[END_REF]. Having a cloud data warehouse is essential for making fast, informed decisions. When necessary, you may extract insightful information from current, accurate, and enriched data thanks to its enhanced computing capabilities and streamlined data management.

On-premise data warehouses are more and more expensive as businesses gather more data. Expanding a business intelligence program significantly increases expenses because storage and computing cannot be acquired separately for on-premises systems. In contrast, data warehouse teams can purchase as much or as little processing power and storage as needed with cloud data warehouses. Furthermore, there is no need for networking, server rooms, or other hardware when using cloud data warehousing.

Cloud data warehouses are crucial in keeping up with increasing data sources. Businesses must combine ERP, CRM, social media, support, and marketing data while preserving speed and performance to make data-driven choices [START_REF] Sundarakani | Big data driven supply chain design and applications for blockchain: An action research using case study approach[END_REF].

III. SECURITY AND PRIVACY CONCERNS IN CLOUD-BASED DATA WAREHOUSING

A. Overview of Security Challenges

Many companies are moving their workloads to the cloud to boost productivity and streamline processes. However, cloud computing can give businesses a competitive edge. However, it's crucial to proceed cautiously while implementing it before completely comprehending the associated risks. When shifting operations to these dynamic environments, a company might fail because it is unaware of the risks and recommended practices for cloud security.

Numerous security issues and challenges are associated with cloud computing. For example, a third-party provider gets data via the Internet and stores it in the cloud. This suggests that control and visibility over the data are restricted.

It also raises the question of how secure it can be in an efficient manner. Everyone needs to be informed about their roles, the threats to cloud security, and the best practices for protecting the cloud.

Cloud computing is still revolutionizing customer offers and internal corporate procedures. Organizations can now more effectively establish remote working environments than previously, owing to the development of cloud computing architecture. It also gives teams the information and resources they need to work together.

B. Overview of Privacy Challenges

The information technology industry's fastest-growing section is cloud computing, which started as a business concept. Customers' biggest concerns about cloud computing security are still tied to privacy and security concerns regarding data on the cloud. These concerns are what keep cloud computing services from taking off.

These challenges include privacy exposure, data leakage, and user data loss [START_REF] Sun | Security and privacy protection in cloud computing: Discussions and challenges[END_REF]. Several cloud computing and service models are used to increase a company's profit, financial growth, and return on investment. Cloud computing provides an appropriate environment and extra benefits or advantages for businesses or government organizations operating on the cloud. As a result, it has several issues with data security and privacy. The third parties manage and store the user's data offsite.

IV. LITERATURE REVIEW

Security

Challenges in Cloud-Based Data Warehousing: The research study [START_REF] Kahn | Migrating a research data warehouse to a public cloud: challenges and opportunities[END_REF] emphasized the role of migrating a research data warehouse to a public cloud by highlighting significant opportunities and challenges [START_REF] Kahn | Migrating a research data warehouse to a public cloud: challenges and opportunities[END_REF]. This study aimed to determine the link of data warehouses with genomic pipelines so that complex data-driven strategies could be understood deeply. The researchers conducted a descriptive study where the primary materials were included, such as actual billing records of people, analysis documents, etc. This research showed that the challenges related to storage architectures, networks, computing, etc., occur in cloud-based data warehousing. If an organization wants to enjoy the benefits of such data warehousing, it is also important to deal with these challenges.

Similarly, another study [START_REF] Tabrizchi | A survey on security challenges in cloud computing: issues, threats, and solutions[END_REF] also explained the security challenges related to cloud-based data warehousing. The researchers explained that cloud computing offers several benefits to different types of organizations, such as simplified IT infrastructure and management [START_REF] Tabrizchi | A survey on security challenges in cloud computing: issues, threats, and solutions[END_REF]. Large networks can be created with the help of cloud computing and an internet connection, but some security challenges are associated with such technologies. In this research study, the researchers surveyed to determine the cloud security issues. The findings of this study show a new classification of security solutions and explore different types of security threats related to cloudbased data warehousing. Privacy Concerns and Regulatory Compliance: Different studies have been conducted on the privacy concerns and regulatory compliance of cloud-based data warehousing. In this case, [START_REF] Fadhil | Security and privacy issues in cloud computing[END_REF] discussed the privacy and security challenges in cloud-based data warehousing. According to this study, the Internet is a vital resource in sharing data, whereas cloud computing offers certain hardware and software services [START_REF] Fadhil | Security and privacy issues in cloud computing[END_REF]. The researchers have indicated that cloud-based data warehousing has been proven to be an important technology to improve certain organizations' operations. However, certain risks should be noted along with the benefits, including privacy challenges. These challenges mainly include compliance with jurisdiction, data protection regulations and data residency. In this case, organizations should take precautions in implementing enhanced privacy-preserving data techniques to avoid any privacy issues.

Authentication and Authorization Mechanisms:

Authentication and authorization mechanisms related to data computing are an important area of the research. In this case, [START_REF] Arora | Mechanism for securing cloud based data warehouse schema[END_REF] discussed certain mechanisms of cloud-based data warehousing, mainly focusing on authorization and authentication. According to the researchers, it is best to use encryption techniques to protect the data warehouse in cloud networks [START_REF] Arora | Mechanism for securing cloud based data warehouse schema[END_REF]. It is important to create several firewalls and encryption codes to secure the data, as it can lead to the leaking of information and malware attacks. Therefore, the researchers have proposed a mechanism through which several encryption codes can be developed to protect the data, such as using dimension tables. The research findings suggested that an enhanced encryption model is the best way to protect the cloud network for data warehousing as it helps encrypt all the column names with the help of keys from a secure host.

Similarly, another research was carried out to understand the mechanisms of authorization and authentication in cloud security. In this case, [START_REF] Chandra | Authentication and authorization mechanism for cloud security[END_REF] computed that cloud computing is an important network source that offers its users a high level of reliability. When users upload their data to cloud computing, it is stored in a data center [START_REF] Chandra | Authentication and authorization mechanism for cloud security[END_REF]. However, it could be a more secure place as it can lead to data theft, privacy issues and data loss. During the study's findings, the researchers indicated that it is important to focus on identity management mechanisms to cope with the challenges of using cloud-based data warehousing. It is important to imply the protocols related to authentication and authorization in cloud computing architecture.

Emerging Trends in Cloud Security: Different trends have emerged in cloud security, which are necessary to understand and discuss. For this purpose, [START_REF] Taleb | Cloud computing trends: A literature review[END_REF] discovered the emerging trends of security in cloud computing. The researchers mainly indicated that these trends are highly emerging in cloud computing in the computer industry. It also plays an important role in improving the overall welfare of an organization. In this study, the issues and challenges related to the increase in the number of users who want to adopt this technology have also been discussed. The researchers mainly used a mixed approach to collect the data and analyze the findings, which indicated the latest and future trends of cloudbased data warehousing. Some important trends related to cloud computing today are hybrid cloud computing, cloudfriendly enterprise frameworks, and using 547EB of data [START_REF] Taleb | Cloud computing trends: A literature review[END_REF]. The researchers also suggested that organizations hire experts to defend the data against hackers firmly. However, the researchers also indicated some future trends in cloud computing, including quantum computing, hybrid cloud solutions, mobile cloud computing, and automation. [START_REF] Nuvento | Emerging trends in cloud computing -Hybrid Cloud, Mobile enablement and much more[END_REF] Some other researchers have also discussed the emerging trends in cloud computing. For example, [START_REF] Li | A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments[END_REF] indicated different levels of cloud-based data warehousing, which are important to understand to perform daily operations efficiently. Cyber attacks have become common; hackers usually steal information from government organizations and private associations [START_REF] Chandra | Authentication and authorization mechanism for cloud security[END_REF]. One of the important reasons behind these attacks is the usage of wireless communication systems. Nowadays, people usually depend on electronic technology, but protecting that technology from cyber-attacks is a major issue. To resolve this problem, the study has suggested some of the latest developments, such as using cyberspace, as it is considered one of the important sources of power in the third millennium [START_REF] Li | A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments[END_REF]. Furthermore, implementing different types of cyber security, such as network, cloud, application, and information security, can also help secure the data.

User Awareness and Training in Cloud-based Data Warehousing:

Training is important in cloud-based data warehousing. Organizations need to develop different training programs in which they can train their employees and users to protect their data. In this case, [START_REF] Tanga | Usage of Cloud Storage for Data Management in the Built Environment[END_REF] discussed that training the users plays an important role in enhancing the environment of the organization and using cloud-based data warehousing. Private or government organizations usually depend on their data, so keeping that data safe and secure is necessary [START_REF] Tanga | Usage of Cloud Storage for Data Management in the Built Environment[END_REF]. The organization must provide accurate information to the users on how they can maintain the confidentiality of their data from any unauthorized access. The main purpose of this research was to indicate the significance of cloud storage and data management. However, the researchers discussed in their findings that data storage is important for data protection and record [START_REF] Tanga | Usage of Cloud Storage for Data Management in the Built Environment[END_REF]. Therefore, it is necessary to give users proper training on protecting their data from bad data storage.

Another research was also conducted related to the importance of training for cloud-based data warehousing. In this case, [START_REF] Yeo | Human factors in electronic health records cybersecurity breach: an exploratory analysis[END_REF] discussed the importance of human beings as they play a vital role in maintaining the privacy of a large amount of data. The researchers mainly focused on the data of healthcare organizations. They indicated that these organizations are facing problems securing their data as a large number of data breaches have occurred worldwide. In this study, the researchers have advised healthcare organizations to focus on implementing technical precaution measures for their electronic health records. It could be done by improving human behavioral interventions and introducing related training programs. The researchers mainly used exploratory analysis to conduct the results in which they showed that poor human security can lead to a large amount of data theft. It can also affect the previous records of healthcare organizations [START_REF] Yeo | Human factors in electronic health records cybersecurity breach: an exploratory analysis[END_REF]. The research findings indicated that user awareness is the reason behind poor data security and phishing scams. In this case, it is important for the organization not just to hire security experts but also to give training to the users regarding data security and management.

V. PRIVACY ISSUES IN CLOUD DATA WAREHOUSING

A. Personal Data Protection

Cloud data warehousing requires the implementation of personal data protection measures. In this case, companies must implement different measures, such as adhering to legal requirements, using data minimization techniques, etc. All such actions help in gaining the trust of the clients. Legal Frameworks and Compliance: Technology firms should comply with legal frameworks to avoid security issues. The California Consumer Privacy Act is one such law that requires firms to protect the privacy of consumers and implement relevant security procedures [START_REF] Harding | Understanding the scope and impact of the California consumer privacy act of 2018[END_REF]. Another important law is the GDPR or the General Data Protection Regulation. It also requires companies to use data protection measures to protect private information.

Data Minimization and Purpose Limitation: Data minimization mainly relates to collecting minimum data from the clients. In this way, the chances of personal data breaches are reduced to a great extent. Therefore, firms should only ask for necessary data from the clients and avoid asking for unnecessary personal information. This is mainly called purpose limitation, which reduces the likelihood of breaches.

Encryption and Anonymization: Encryption mainly relates to implementing strong passwords or codes for accessing data. Thus, only authorized persons with the relevant passcodes can access the data. On the other hand, anonymization methods involve changing or eliminating clients' personal data. Both of these techniques protect the visibility of private data.

User Consent and Transparency: It is also highly significant for firms to gain consent from individuals and establish transparency measures while dealing with private data. In this case, they should always gain consent from clients before using their data for any purpose [START_REF] Ali | A framework strategy to overcome trust issues on cloud computing adoption in higher education[END_REF]. The clients should be informed of the purpose of using their data so that they can make an informed decision. In this process, transparency also helps in developing a strong level of trust among the clients. This way, they trust the firm with their private information and do not hesitate to share their data.

B. Data Ownership and Control

Data ownership is also an important method in cloud warehousing. Firms must identify data ownership before granting access to anyone. This level of control of the data assists in protecting the data in every way.

Ambiguities in Cloud Environment:

There are also some issues in cloud-based data related to data ownership. Sometimes, some pieces of information are shared by two or more users. In such situations, it takes work to identify the right owner. Companies should implement this practice very carefully to avoid data breaches.

Figure 6: Ambiguities in Cloud Environment [START_REF] Kazim | A Review: A Survey on Privacy Preserving for Secure Cloud Storage[END_REF] Defining Access Controls and Responsibilities: Companies should always implement access controls to avoid data breaches [START_REF] Mohammed | The interaction between artificial intelligence and identity and access management: an empirical study[END_REF]. In this case, access should be provided only to the relevant personnel. Not all kinds of data should be accessible to everyone. Overall, access controls help develop the proper organization of data while maintaining the highest levels of security.

Contractual

Agreements and Service Level Agreements: It has been observed that using service level and contractual agreements also helps firms in overcoming security issues. These agreements help adhere to strict rules regarding using specific data. Therefore, no hackers or outsiders can access the data easily since strong security measures are adopted in compliance with the relevant agreements.

C. Adhering to Privacy Regulations

Companies implement privacy rules and regulations to ensure that clients' private information is reserved. No personnel member is allowed to access clients' private data without permission. Otherwise, legal action can be taken against anyone trying to breach the privacy rules.

Today, many firms adhere to global privacy regulations like the GDPR. It requires all companies to invest in strong privacy practices [START_REF] Rustad | Towards a global data privacy standard[END_REF]. Another regulation in this regard is the Health Insurance Portability and Accountability Act. It also demands appropriate methods to maintain the accountability of sensitive data.

In compliance with these laws, companies also use innovative techniques like data classification and handling. These methods divide data into different categories based on their importance and sensitivity. In this case, many companies use methods like data masking and encryption. This is because they help in developing effective privacy rules.

VI. SECURITY AND PRIVACY MEASURES IN CLOUD-BASED DATA WAREHOUSING

A. Encryption and Data Masking

For companies involved in cloud data warehousing, it is very important to implement techniques like data masking and encryption. This is because, today, it has become a trend to hack people's sensitive information and use it for negative purposes. Therefore, companies must be highly conscious of personal data and use encryption to protect sensitive data. Encryption: Encryption is a very common but highly important technique in cloud-based data warehousing. In this method, the sensitivity is converted into an unreadable format. Therefore, hackers cannot read the data even if they gain access to it. This method has become popular among many firms and grants high security.

Data Masking: Data masking mainly relates to "masking" or "hiding" the private data in the cloud. For this purpose, a secure database is usually created. The data is not visible to hackers, who cannot leak personal information.

B. Access Controls and Authentication

It is crucial for technology firms to always focus on access controls of private databases. In this regard, an authentication procedure is utilized, which helps in protecting personal data. Strong passwords and other security protocols that cannot be easily breached are used in such cases. The use of access controls assists in limiting the number of people using a specific dataset. The fewer people use the data, the fewer the chances of breaches. This concept is very important to understand in the area of cloud warehousing. Only a single breach can put the firm's reputation at stake.

Companies should also use the authentication technique to secure their reputation in the industry. In this case, verification is always required when someone accesses the private data. With verification, access is granted to everyone. The verification can be done through different methods like strong passwords, biometrics, etc. All such measures add a level of security to the cloud data. Warehousing [START_REF] Lee | A Work in Progress: Context based Encryption Scheme for Internet of Things[END_REF] C

. Auditing and Monitoring

Companies involved in cloud data warehousing should also be aware of auditing and monitoring procedures [START_REF] Kumar | On cloud security requirements, threats, vulnerabilities and countermeasures: A survey[END_REF]. These methods help implement transparency in security measures and detect areas of weakness. Regular auditing and monitoring procedures are crucial in identifying areas with weak security. In this case, strong security measures are immediately implemented in such areas to avoid any future breaches.

Comprehensive Audit Trails: As discussed earlier, detailed audit trails help companies track every single action taken related to the use of cloud data. Even a slight modification in sensitive data is detected immediately, and audit trails are conducted to identify the problematic areas. In this way, security violations are immediately detected, and strong security measures are put in place on time before any huge breaches.

Real-Time Monitoring:

Companies should also implement real-time monitoring of cloud data warehousing [START_REF] Knebel | A study on cloud and edge computing for the implementation of digital twins in the Oil & Gas industries[END_REF]. If any unauthorized access occurs to sensitive data, it should be detected and monitored in real-time. The patterns of data access should be regularly monitored to identify the presence of hackers anywhere in the cloud.

Incident Response Readiness: Regular auditing mainly helps companies in preparing for any upcoming data breach incidents. Since audits help in identifying all the weak areas, the chances for future incidents of breaches are highly reduced. This is a proactive approach that helps companies develop effective strategies for the mitigation of security breaches.

VII. CHALLENGES IN IMPLEMENTING SECURITY AND PRIVACY MEASURES

Cloud-based data warehousing is a simple model for storing large data. However, it poses certain challenges when implementing security and privacy measures. One of the important challenges is saving the data from hackers and cyberattacks. The attackers mainly target the data sources and damage the whole system, which can compromise sensitive information and cause reputational consequences to the organization. In this case, it is important to discuss these challenges in detail.

A. Complexity of Cloud Environments

The cloud computing system is a critical system that is difficult for certain users to understand. The complex system of the cloud environments makes it a big challenge to implement security and privacy measures in cloud-based data warehousing systems. One of the main reasons is the multifaceted nature of the cloud infrastructure, which makes it more complex. It mainly involves diverse service models, shared resources, dynamic scalability and complexity of the navigation process. Service Models: Cloud computing involves certain service models, making its Infrastructure more complex. Some models are Infrastructure as a Service (PaaS), Software as a Service (SaaS), etc. Each model has its challenges as they present unique security considerations. The organizations must understand the diversity of each of the service models before the implementation process.

Shared Responsibility

Dynamic Nature and Scale: Cloud computing or cloud environments have a dynamic nature and scalability, which poses certain challenges in maintaining privacy and security controls. The resources in the cloud system are measured on different scales according to the user's demand. However, its dynamic nature makes it difficult and complex for the organization to ensure security measures, which require automation and continuous monitoring.

Interconnected Ecosystems: A cloud computing system usually uses a network of interconnected ecosystems such as platforms, infrastructure components and applications to provide an integrated cloud computing environment. This creates a huge challenge for imposing security and privacy measures, as securing the interconnectedness of vulnerabilities across the system is difficult.

B. Integration with Existing Systems

Organizations use cloud-based data computing warehousing systems to consume a large amount of data. Cloud-based data warehousing is an integrated system, which makes it more important. This Integration poses a firm challenge in the security and privacy implementation process. In this case, the organization needs to harmonize with all security protocols to make its system more cohesive and secure.

Diverse Technological Stacks:

The systems that the organizations are already using operate on diverse technological stacks, each with a unique security framework. In this case, it is critical to understand each security framework while implying cloud-based data warehousing to maintain the security integrity of both cloud environments and legacy.

Data Migration Challenges:

In cloud computing systems, a certain amount of data is migrated from legacy systems to the cloud. It mainly presents the complexities regarding the preservation of data integration during the transition. Therefore, organizations must ensure that sensitive information remains protected during migration, as it can lead to unauthorized access and data breaches.

C. Resource Constraints

The resource constraints are also a great challenge in implementing security and privacy measures in cloud-based data warehousing. The main reason is the budgeting system, technological incapabilities, and skilled personnel, which greatly challenge maintaining a comprehensive security posture.

Budgetary Limitations: Usually, medium or small-sized enterprises face budgeting challenges when implementing a new cloud-based system. It is difficult for them to allocate sufficient financial resources for robust security measures. To overcome this challenge, enterprises should focus on comprehensive security strategies such as employee training, continuous monitoring systems and smart investments in advanced tools.

VIII. LATEST TRENDS IN CLOUD SECURITY

Zero Trust Model: Different trends and models have been introduced to help secure information in cloudbased data warehousing. The zero trust model is one of the important and efficient models used to secure the data [START_REF] Mehraj | Establishing a zero trust strategy in cloud computing environment[END_REF]. The model's main function is to pose strict identity security on the device. So, when a user wants to access resources on a device, they must undergo a strict identity verification process on a private network, whether outside or within the network's perimeter. The main principle behind this model is continuous monitoring and validation. It not only verifies the user's identity but also verifies the privileges and device security. Moreover, it also establishes a certain number of logins and connections time out, forcing the users to log out and verify themselves again after a certain amount of time. In this modern world of technology, it has become important for government and private organizations to implement this model.

Implementation of Artificial Intelligence and

Machine Learning: Artificial intelligence has now become a vital part of data processing and securing. It can automate complex processes and decrease the downtime of finding the data by indicating maintenance needs [START_REF] Mohanta | Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology[END_REF]. It has improved the quality and effectiveness of the employee decision-making processes. Therefore, it is necessary to understand the importance of artificial intelligence and machine learning in securing the cloud infrastructure of certain organizations. Through AI and machine learning, organizations can easily automate security concerns as they can handle a large amount of data. AI automatically scans any threat or scamming and enhances the protection measures. Furthermore, it is also important for organizations to use AI to manage network vulnerabilities as it identifies the weak points of security and helps to focus on critical security tasks [START_REF] Bécue | Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities[END_REF]. It also handles repetitive security tasks easily by detecting security threats daily. By implementing AI, organizations can avoid certain errors that usually occur to humans or their boredom.

Cybersecurity Mesh: Cybersecurity mesh is another important emerging trend in cloud-based data warehousing. It is an important approach used in security control systems [START_REF] Samanta | A survey of wireless battery management system: Topology, emerging trends, and challenges[END_REF]. However, it is more efficient and flexible when it comes to hybrid multi-cloud infrastructures. Nowadays, different organizations are looking forward to growing in the global cybersecurity market. In this case, they are focused on expanding the business and resources outside the localized perimeters without compromising network security. In this case, a cybersecurity mesh can be an important tool to deal with data theft threats as it extends security in a hybrid Infrastructure, allowing all points of access and systems to be secured with a unified set of technologies. The consolidated dashboards in the cybersecurity mesh make it more responsible and effective. Therefore, the organization must use this tool. Moreover, data can be easily managed and controlled by taking advantage of this latest technology.

Secure Access Service Edge: Cloud-native security features like firewalls as a service, zero-trust network access, cloud access security brokers, and secure web gateways are all provided by the Secure Access Service Edge (SASE) architecture, which combines network and security as a service capability [START_REF] Van Der Walt | Research gaps and opportunities for secure access service edge[END_REF]. This functionality is offered as a service by the SASE vendor and is delivered from the cloud.

Through the combination of several security features, a software-defined wide area network (SD-WAN) or other WAN is secured as a whole for network traffic through the use of a SASE architecture [START_REF] Swamy | A Study on Security Attributes of Software-Defined Wide Area Network[END_REF]. If that's where users are, then legacy methods of inspection and verification-like sending traffic to data center firewalls via a multiprotocol label switching/forwarding service-work well.

IX. EMERGING TECHNOLOGIES AND THEIR IMPACT

Integration Platform-as-a-Service, or iPaaS, for Integration: iPaaS is used by large enterprises to integrate applications and data that reside in both public and private clouds and on-premises [START_REF] Trajkovska | Automation and Monitoring on Integration ETL Processes while Distributing Data[END_REF]. This makes it possible to use SaaS as a technological bridge for shared databases to create and implement intricate integration projects involving two or more connections. Traditional ETL gathers data from various systems into a single database, data store, and data warehouse to analyze and inform business decisions. Data sharing via API endpoints and security via data and authorization-based API policies are possible with iPaaS systems.

NoSQL for Big Data: When a database isn't structured and contains data in different formats, such as text, images, or videos, creating a schema can be difficult [START_REF] Matallah | Comparative study between the MySQL relational database and the MongoDB NoSQL database[END_REF]. As a result, schema-less alternatives that offer more flexibility than SQL solutions have been developed to handle unstructured or big data. Since this is document-oriented rather than table-oriented (as in SQL), it is more flexible than traditional databases. Particularly when the data in the data warehouse is unstructured and changes often, big data, highvolume databases, and a wide range of online applications are well suited for NoSQL because a fixed schema model does not limit it and because it can scale horizontally, increasing compute and storage capacity.

Column-Based Storage for Advanced Analytical Query:

The best choice for storing data in a cloud data warehouse for advanced analytics is column-based storage or columnar database [START_REF] Liu | A big data lake for multilevel streaming analytics[END_REF]. This database management system stores data in columns instead of storing data in rows, as most relational databases do. In other words, every column in a table is kept independently, frequently in adjacent memory regions.

A columnar database's primary goal is to increase the effectiveness and speed of processes that require reading massive amounts of data. Different optimizations are possible because each column's data tends to be the same. Because the data in a column is comparable, columnar storage, for example, provides for improved data compression and more effective querying and aggregation, particularly in analytical and reporting jobs.

X. CONCLUSION

In conclusion, cloud-based data warehousing is an effective way to store large data. It helps in fulfilling the demands of the users in terms of securing the data. However, it comes with different security and privacy challenges that must be addressed to build the clients' trust. One of the important ways to maintain data integrity is data ownership, contractual agreements and access controls. However, different security and privacy challenges create hurdles for organizations in maintaining data integrity. Some challenges occur due to complex computing systems, integration issues and resource constraints. In this modern world of technology, many new technologies, such as iPaaS, column-based storage and NoSQL, offer innovative solutions to these challenges.

Moreover, cloud computing is dynamic and needs continuous monitoring and proactive security measures. Organizations should embrace advanced technologies and follow robust security protocols to cope with these challenges. This can help them ensure the integrity and confidentiality of the information in cloud-based data warehousing.

Figure 1 :

 1 Figure 1: Cloud Data Warehouse [3] B. Importance in Modern IT Infrastructure

Figure 2 :

 2 Figure 2: Cloud Security Challenges[START_REF] Maurya | Data is shifting to cloud[END_REF]

Figure 3 :

 3 Figure 3: Security Risks and Challenges for Cloud Computing[START_REF] Edviser | Eight Risks And Challenges For Cloud Computing[END_REF]

Figure 4 :

 4 Figure 4: Emerging Trends in Cloud Computing[START_REF] Nuvento | Emerging trends in cloud computing -Hybrid Cloud, Mobile enablement and much more[END_REF]

Figure 5 :

 5 Figure 5: Personal Data Protection in Cloud Computing[START_REF] Consiliant | Data Protection, Backup & Recovery[END_REF]

Figure 7 :

 7 Figure 7: Data Masking in Cloud-Based Data Warehousing[START_REF] Gupta | THE NEED FOR DATA MASKING IN A DATA WAREHOUSE[END_REF]

Figure 8 :

 8 Figure 8: Access Control and Authentication in Cloud-based DataWarehousing[START_REF] Lee | A Work in Progress: Context based Encryption Scheme for Internet of Things[END_REF]

Figure 9 :

 9 Figure 9: Challenges in Implementing Security and Privacy Measures[START_REF] Masood | Cloud authorization: exploring techniques and approach towards effective access control framework[END_REF]

  Model: Shared responsibility is a complicated cloud security framework in which different security obligations of cloud computing are dictated. The main function of this model is to provide its users with the surety of accountability. One of the simple examples of this model is teaching the students both subject and pedagogical knowledge.

Figure 10 :

 10 Figure 10: Shared Responsibility Model in Cloud-Based Data Warehousing[START_REF] Rewind | The Shared Responsibility Model and SaaS, explained[END_REF]


Ousseynou Kane Diallo

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Morphology of damage occurring during decompression in a hydrogen-exposed EPDM

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ABSTRACT: A polymer exposed to a diffusing gas can be damaged by cavitation and/or cracking when the gas sorbed in the polymer expands faster than it desorbs out of the material. This phenomenon was reported several decades ago, but few studies aimed to quantify the morphology of cavitation damage, especially the statistics of cavity fields, and especially in hydrogen. This work addressed the time evolution of (i) cavity fields (number, size and spatial distribution) and (ii) the inflation/deflation kinetics of single cavities, in a transparent EPDM exposed to 9 MPa hydrogen pressure, saturated for 1 h and submitted to a 2.5 MPa/min gas decompression rate. Statistical analysis was based on a covariogram method applied to successive pictures taken all along the decompression test. Isotropy and time evolution of the phenomenon could be discussed at the macroscopic scale and at the morphological Representative Elementary Volume scale (REV).

been widely characterized and quantified. Interactions effects between neighboring cavities need to be highlighted too.

The aim of this study was first to investigate the evolution of a single cavity (inflection and deflection kinetics) in elastomer matrix and to look into possible interactions with its neighboring cavities. The second aim was to address the statistical evolution of cavities fields over time and over decompression conditions. A mathematical tool (covariogram method) was used for statistical analysis. The work was conducted in a transparent unfilled EPDM.

MATERIAL AND METHODS

Material and sample

This study was carried out in an unfilled EPDM, provided by Pr. Nishimura from Hydrogenius Laboratory at Kyushu University. Since this material was transparent, direct observation of the damage during decompression test was possible using visible light transmission all along the test.

Two types of test specimens were cut from molded sheets (147 × 147 × 2 mm): rectangular (30 × 8 × 2 mm) and square specimen (20 × 20 × 2 mm). They were designed considering the reduced size of the decompression chamber and the difficulty to have an optical focusing. The influence of the sample shape will be discussed later in the paper.

INTRODUCTION

Ethylene-Propylene-Diene-Monomer (EPDM) elastomer is widely used in many industrial applications. EPDM exhibits excellent resistance to weather, ozone, acids and thermal degradation. These properties make it a potential material for barrier and sealing in the automotive, electrical and industrial applications. For example, EPDM can be used in the O-rings of seal equipment under high-pressure gas (CH4, H2S, H2 …). Therefore, it is important for manufacturers to investigate the influence of hydrogen on the strength properties and damage of this material [START_REF] Busse | Physics of rubber as related to the automobile[END_REF].

Indeed, after exposure to diffusing gas for a long time, elastomer undergo internal fracture (nucleation of bubbles, forming or cracks) when the high pressure is suddenly released. This phenomenon is often referred to "explosive decompression failure" but also happens at slow decompression rates [START_REF] Briscoe | Explosive decompression failure of rubber: a review of the origins of pneumatic stress induced rupture in elastomer[END_REF][START_REF] Yamabe | Blister fracture of rubbers for O-ring exposed to high pressure hydrogen gas[END_REF]). Most of the reported works addressed the damage sensitivity to decompression conditions [START_REF] Yamabe | Blister fracture of rubbers for O-ring exposed to high pressure hydrogen gas[END_REF][START_REF] Jaravel | On key parameters influencing cavitation damage upon fast decompression in a hydrogen saturated elastomer[END_REF]. From a mechanical point of view, cavitation criteria were widely inspired from purely mechanical cavitation modeling [START_REF] Yamabe | Blister fracture of rubbers for O-ring exposed to high pressure hydrogen gas[END_REF]. More recently, it was evidenced that critical pressure criteria were not relevant to depict cavitation onset in diffusomechanical coupled conditions (Jaravel 2013).

Damage morphology and evolution at the local scale (of a single cavity or a small number of close cavities) and at the full cavity filed scale has not 1 2.2 Experiments Tests were performed in an INSTRON hydraulic tensile machine fitted with a pressure vessel allowing gas pressure up to 40 MPa, between the ambient temperature and 150°C. The door of the chamber included a central window to track damage at the optically visible scale. For safety reason related to hydrogen, the volume of the chamber was small (1.77 L with a diameter of 150 mm and length of 100 mm). More information on this device can be found in previous papers (Castagnet et al. 2002). To avoid oxygen/hydrogen mixture in the pressure cell before starting the experiment, three successive pressurization/depressurization cycles were performed by introducing nitrogen up to 1 MPa. In the present paper, all samples were initially at ambient temperature. Pressurization and decompression stages followed pressure ramps with constant pressure rates of 1 MPa/min and 2.5 MPa/ min respectively. Figure 1 illustrates the pressure cycle applied to samples. Samples were clamped at one end and only guided at the other end to stay in the optical plane, without any stretching during the test. In order to estimate reproducibility, 4 samples were submitted to the same decompression conditions: 2 rectangular samples introduced at the same time in the chamber (like for any decompression condition) and 2 square samples (2 different tests). They were exposed to hydrogen gas at a maximum pressure saturation of P sat = 9 MPa for 1 h.

After exposure, the hydrogen gas was decompressed down to the atmospheric pressure. During test, the temperature and the pressure in the vessel were recorded every second.

Image processing

Since the vessel was equipped with glass windows, damage onset and morphology could be tracked all along the test. It was observed under visible light transmission, using a wired light and a Sony XCD SX 90CCD camera fitted with an Avenir TV Zoom Lens 12.5-75 mm F18. There was never any visible damage in the sample during pressurization and gas saturation. Pictures were taken at a rate of one picture per second during and after decompression. The precision of detection was estimated to 26 µm i.e. the first detected cavity had a 26 µm diameter (equal to 1 pixel). The detection of the first cavity was done by image subtraction: the first picture at the beginning of decompression was subtracted to all pictures taken after this time. Time was counted from the beginning of decompression and t cav was the time elapsed between the beginning of decompression and onset of the first visible cavity.

Two types of information could be extracted from image analysis during the decompression test: (i) the inflation and deflation rate of single large cavities selected at different places in the sample, and (ii) features of the full-field of cavities (number of cavities, size distribution and spatial distribution). Figure 2 illustrates the image processing previously applied to pictures. Due to the boundary layer of the sample where no cavity was detected (Jaravel et al. 2013), the processed area was reduced to a window (21 × 6 mm for the rectangular specimens and 19 × 19 mm for the square specimens). Image processing followed two steps:

-Binarisation: pictures were thresholded to pick out cavities as represented in Figure 2b. An erosion process was applied to remove dust or initial defects that should not be included in cavity analysis.

-Reconstruction: After binarisation, some cavities may have lost regular shape because of the grey level difference. A reconstruction algorithm, involving the thresholded picture and the original one, was used to restore cavities to their initial shape. This algorithm approximated cavity as a spheres.

From this reconstruction, centroids and cavity diameter could be deduced. The number of cavities and size (average and distribution) could be plotted as a function of time. The diameter evolution of single cavities could be picked up too.

Statistical analysis of cavity fields

In order to quantify the spatial distribution of cavity fields and its evolution with time, a statistical analysis was applied to reconstruct pictures. Analysis was based on covariogram building [START_REF] Bez | Covariogamm: a structural tool[END_REF][START_REF] Cressi | Spatial statistics[END_REF]. In micromechanical modelling, covariograms are often used as a statistical analysis to estimate the features or mor-phological Representative Elementary Volumes (REV), from representative pictures of the initial microstructure. It is defined as the probability C(h) to encounter a cavity at distance h of another cavity along one given direction. A typical example of the obtained function C(h) is displayed in Figure 3. Several parameters could be extracted from such curves:

-At h = 0, i.e. at the center of a given cavity, the probability C( 0 The method was extended here to successive pictures of the microstructure along time. One can refer to (Nait-Ali et al, in press) for more details about the method. All the above listed parameters could be tracked as a function of time during decompression.

RESULTS AND DISCUSSION

Onset time of the first cavity

Important information recorded during decompression tests was the onset time of the first cavity. The experimental device as well as the image processing allowed to determinate t cav . It is reminded that no damage has been observed during pressurization and saturation stages consistently with the literature [START_REF] Stewart | Nucleation and growth of bubbles in elastomers[END_REF].

For reproducibility requirement, the same test at P sat = 9 MPa and P = 2.5 MPa/min was carried out twice, not consecutively. Firstly, the first cavity occurs after the end of decompression: t cav was between 5 s and 14 s after the end of decompression for the rectangular specimens and 16 s after the end of decompression for square ones. It means that in such condition, the beginning of damage could not be governed only by the external loading: cavities appeared while all external parameters (temperature, gas pressure) were constant. After the end of decompression, there was still gas in the sample because gas diffusion was not fast enough for desorption to be completed although pressure in the chamber had returned to atmosphere. By comparing the two types of samples, the onset time of cavitation seemed to be independent on sample wideness. Secondly, cavitation only affected the center of the sample: no cavity was detected near free surfaces (Figure 4b) due to hydrogen diffusion which was fast enough to get out of the sample without damage.

Results in the present EPDM confirmed Jaravel's observations in a silicone rubber [START_REF] Jaravel | On key parameters influencing cavitation damage upon fast decompression in a hydrogen saturated elastomer[END_REF], i.e. t cav decreased as the decompression rate increased or increased with the saturation pressure. Dependence on these two parameters was shown to be non-linear.

Evolution of the cavity fields

Pictures in Figure 4 were taken at different times after the onset of the first cavity. Other cavities appeared at different times and randomly throughout the volume of the sample. Figures 5 and6 display the evolution of the number of cavities per mm and the average diameter respectively, for both sample geometry. First, cavities appeared randomly through the sample with a 25 µm diameter (detection threshold) and then each cavity grew with its own kinetic between t cav and 11 min after the onset of damage; at this time, the number of cavities nucleated reached its maximum. At the end of the inflation step, the biggest cavities exhibited a diameter about 300 µm. Most cavities then deflated and seemed to close. However, largest cavities and crack remained visible even 2 hours after decompression (Figure 4d). In these decompression conditions, there was always less than one cavity per mm on average.

Curves in Figures 5 and6 showed that the number of cavities and average diameter did not depend on the sample geometry, suggesting that rectangular samples were large enough to capture representative fields for these two parameters.

To highlight the inflation/deflation kinetics of single cavities, the diameter of 4 cavities randomly selected in the middle part of the sample (see in Figure 4b) was plotted all along their life cycle in Figure 7. All curves were plotted as a function of the time elapsed since the onset of the first cavity. The inflation kinetics was the same for all cavities. Deflation started at the same time but with slightly different kinetics.

Work is in progress to confront these experimental results to numerical simulations of single cavity inflation and deflation by Finite Element Method.

Spatial distribution

Damage morphology analysis at the cavity field scale was completed by considering the spatial distribution and its evolution with time. Such information is important for further purpose of predictive modeling. The concept of REV for instance is crucial for multi-scale modeling or microstructure numerical calculations. It is then necessary to define more precisely REV dimensions, the number of cavities to be introduced and their distribution over the computed volume. To achieve this, covariograms were built up along the horizontal and vertical directions of processed pictures. Figure 8 and Figure 9 show the time evolution of one of the parameters extracted from covariograms, i.e. the correlation length along the vertical (x) and horizontal (y) direction respectively.

First, it could be noted that curves were almost identical for both sample geometry, regardless the direction of the covariogram, meaning that no anisotropy was introduced at the morphological REV scale by the rectangular shape.

Secondly, by comparing Figure 5 and Figure 6, i.e. the horizontal and vertical direction, it could be deduced that the correlation length was the same in both directions. All along the test, the morphological REV resulted isotropic. D c along axis x and y was equal to 500 µm (measured at 8 mn, when cavities nucleated reach its maximum). Also, it meant that the REV would be a square with size depend on time as follows:

-REV was less than 50 µm in the early stages of nucleation -REV was maximal (approximately 500 µm)

when the number of bubbles was maximal.

The statistical distribution more or less evolved with the average size of the cavity.

In such decompression conditions, the nucleation of new cavities did not affect the statistics of the existing cavity field. It was shown that it was not the case any more for more severe decompression conditions.

The distance D r between cavities appeared weak and relatively independent on time as represented in Figure 10. This distance was more than 1500 µm at the first step of nucleation (few cavities but distant from each other) and became less than 10 µm in advanced stages of damage (many cavities but very close to each other). Interaction between cavities is then expected to be strong and important to take account. However, Figure 10 shows that the distance between cavities along x axis was less than that along y (D r (x) = 5 µm and D r (y) = 10 µm at 8 min after the end of decompression).

Figure 11 focuses on the macroscopic scale by comparing the asymptote value of covariograms along the horizontal and vertical axis.

It could be pointed out that isotropy depended on time. Between the onset of cavitation and 4 min after, asymptotes x and y almost overlapped, meaning that the statistical distribution was macroscopically isotropic in this time period. In fact, there were not enough cavities (less than about 30 cavities) to macroscopically disturb the spatial distribution of cavities. During the following stage, the increase of cavities number added to the fact that each cavity had its own growth kinetics, made the statistical distribution macroscopically anisotropic: asymptote x became larger than asymptote y with a maximum difference of 40%. It meant that the probability of encountering another cavity along x axis was greater than along y axis. Finally, isotropy was recovered later, during the last stage corresponding to the deflation of cavities and to the closure of some of them.

Same results were obtained in the square samples, showing that rectangular samples were large enough to be representative of experiments.

CONCLUSION

The aim of this study was to provide information about the statistics of cavity fields nucleated in an EPDM rubber upon decompression, after hydrogen exposure. Analysis was based on covariogram method applied to successive pictures taken all along decompression tests. The evolution of the cavities number and its average size was shown to depend on time. Covariogram parameters were shown to be time-dependent too.

Two sample geometries (rectangle and square) were tested. Comparison showed that rectangular samples were large enough to capture representative statistical parameters.

At the macroscopic scale, the distribution was shown to be isotropic and homogeneous during the first nucleation stage and at the end of deflation stage (a few tens of cavities in each case). In between, anisotropy appears with a maximum difference about 40% between the vertical and horizontal directions.

At the cavity scale, the onset of new cavities did not affect the statistical distribution of cavities in the reported decompression conditions. REV was found to be isotropic all along the test, with a size almost following the average size of cavities. Consistently, the inflation/deflation kinetics of single cavities randomly picked up in the field was shown to be almost the same.

From another test campaign (not reported in this paper) performed to quantify the effect of decompression conditions, it could be shown that the statistical distribution of cavities depended strongly on the time onset of cavitation (during decompression or after the end of decompression).

  Morphology of damage occurring during decompression in a hydrogen-exposed EPDM O. Kane Diallo, S. Castagnet, J.C. Grandidier & A. Nait-Ali Département de Physique et Mécanique des Matériaux, Institut P' CNRS, ENSMA, Université de Poitiers, Futuroscope Cedex, France

Figure 1 .

 1 Figure 1. Loading cycle pressure of samples.

Figure 2 .

 2 Figure 2. Image processing of sample at P sat = 9 MPa for 1 h and decompress to atmospheric pressure at 2.5 MPa/ min (a) Raw image (b) Binarisation (c) Reconstruction.

  ) was equal to the global surface ratio of cavities. -Far away from this cavity, at large h values, the probability to find another cavity became equal to a low constant value. If this asymptote value equaled the square of the surface ratio, then the distribution could be considered as homogeneous. -In between, parameter D c was the correlation length i.e. distance beyond which the statistical influence became negligible. It could be viewed as a morphological REV. By comparing D c values along different directions, it was possible to get information about the anisotropy of the spatial distribution of cavities within the field. It provided information about the onset of multiple cavities populations. -The repulsion length D r corresponded to the average distance between 2 nearby cavities.

Figure 3 .

 3 Figure 3. Example of covariogram and parameters used for statistical analysis.

Figure 4 .

 4 Figure 4. Damage morphology of a sample submitted to P sat = 9 MPa and ɺ P = 2.5 MPa/min (all images were taken after the onset of the first cavity).

Figure 5 .

 5 Figure 5. Evolution of cavities number since the onset of damage: square sample (circles), rectangular sample (lozenges).

Figure 6 .

 6 Figure 6. Evolution of the average of cavities since the onset of damage: square sample (circles), rectangular sample (lozenges).

Figure 7 .

 7 Figure 7. Evolution of the diameter of some cavities tracked during the cavitation damage: cavity A (triangles), cavity B (lozenges), cavity C (circles), cavity D (square).

Figure 8 .

 8 Figure 8. Evolution of the correlation length along x axis since the onset of damage: square sample (circles), rectangular sample (lozenges).

Figure 9 .

 9 Figure 9. Evolution of the correlation length along y axis since the onset of damage: square sample (circles), rectangular sample (lozenges).

Figure 10 .

 10 Figure 10. Zoom on the evolution of the repulsion distance with rectangular sample since onset of damage along x axis (triangle) and y axis (square).

Figure 11 .

 11 Figure 11. Evolution of the asymptotes since onset of damage along x axis (dash with cross point) and y axis (dot line).

ACKNOWLEDGEMENTS

Authors would like to gratefully acknowledge Pr. Nishimura, Dr. Fujiwara and Dr. Ono, from Hydrogenius Laboratory at Kyushu University, for fruitful discussion. French Ministery for Education and Research is also acknowledged for granting O. Kane-Diallo's PhD.

This work pertains to the French Government program "Investissements d'Avenir" (LABEX INTERACTIFS, reference ANR-11-LABX-0017-01).