Improving Privacy Governance in A Big Data Environment in The Kingdom of Saudi Arabia
Hessah AlMansour, Najla AlOsaimi, Omer Alrwais
King Saud University, Riyadh, Saudi Arabia.
*Corresponding Author E-mail: oalrwais@ksu.edu.sa
ABSTRACT:
Big data has emerged as a popular academic area in recent years. The growing volume of data raises the possibility of violating people's privacy. There are several privacy-preserving strategies established for privacy protection at various phases of data lifecycle (e.g., data production, data storage, and data processing) of a massive data set. The purpose of this study is to provide a detailed review of the privacy preservation strategies in big data.
KEYWORDS: Big Data, Privacy, Security, Data Lifecycle, De-Identification, k-Anonymity, l-Diversity.
INTRODUCTION:
Nowadays, there is a rapid increase in technology, and computer updates are frequently upgrading the security of computers from any pilfering. The data today is getting bigger in the healthcare sector, telecommunications, energy, pharmaceuticals, and many more. These big data are now measured in zettabytes2. In 2005, big data captured attention and described a broad category of extremely huge data sets that are so enormous and complicated that they are nearly impossible to handle and analyze with conventional data management techniques1.
Incidents involving information security can directly impact business operations and procedures. For instance, a corporation that develops and manages a worldwide value chain must extensively exchange information assets with its business partners. Without a doubt, to increase and preserve their competitiveness, businesses require a safe mechanism for exchanging information assets. We should be aware that information security procedures are closely related to the company value and that information assets management must be a key business strategy component 3.
Big Data is an accumulation of huge data sets with voluminous, intricate data within them. The term "big data" describes information "that surpasses the processing capability of traditional database systems." The information is either too large, moves too quickly, or is not structured to meet your database design 2.
The policies and practices implemented to manage data inside an organization are referred to as data governance. "A group of procedures that guarantee significant data assets are legally handled across the organization" is the definition of "data governance." When it comes to making the correct judgments, data governance makes sure that the appropriate individuals have access to the relevant types of data at the appropriate times.2
The three Vs can be used to characterize big data as shown in figure 1:
Figure 1: 3Vs of big data 4.
Which is created and performed hourly and is regarded as a large amount of data. The amount of data is expanding exponentially. This "volume" component is a part of big data itself 1.
Data is coming in at a never-before-seen rate and must be processed quickly. The necessity to handle massive amounts of data in almost real-time is being driven by RFID tags, sensors, and smart meters. Most businesses struggle to respond fast enough to handle the velocity of data 1.
In which there are several forms available for the data. numerical and structured data produced by financial transactions, email, video, audio, line-of-business applications, and unstructured text documents. We must figure out how to control, combine, and handle these many types of data 1.
According to current technological trends, the foundation of all government transactions in the Kingdom of Saudi Arabia, and the 2030 vision for digital transformation, several technologies ought to be considered and employed in various disciplines 5. The regulation of information security in big data settings has not received enough academic attention, and there is an increasing demand for fresh research and suggestions in this area 2, so the aim of this study is to discuss the challenges in big data security and privacy and how to implement them in the Kingdom of Saudi Arabia.
As shown in Figure 2, the big data today could be categorized into 3 parts:
Figure 2: Various kind of data 6
Text is the primary type of structured data, which is easily processed. It's simple to enter, save, and evaluate these data. A language known as "structured query language" (SQL) makes it simple to manage structured data, which are stored as rows and columns 6.
XML, JSON, and emails are examples of semi-structured data. Relational databases, which describe data via edges, labels, and tree structures, are not appropriate for semi-structured data. Trees and graphs are used to illustrate this, and they contain properties and labels. These data don't have any schema. Semi-structured data can be stored in graph-based data structures 6.
Figure 3: Attributes of semi-structured data 6
Audio, video, and picture data are examples of unstructured data. 90% of data in today's digital world is unstructured, and this percentage is rising. Relational databases are ill-suited to hold this data, hence NoSQL databases were developed as a workaround.
Figure 4: Attributes of unstructured data 6
Big data often has various types of data, meaning it might include text, audio, images, videos, and other types of content. Variety is a sign of the different qualities of the data. In recent years, a number of solutions have been created to safeguard privacy related to big data. As shown in Figure 5, the stages of the big data life cycle may be used to categorize these mechanisms 7.
Figure 5: The phases of the big data life cycle 7
As shown in Figure 5, access restrictions and data fabrication techniques are employed throughout the data-generating phase to safeguard privacy, and the encryption processes provide the foundation of most methods of privacy protection throughout the data storage phase.
The categories of encryption-based methods include storage path encryption, attribute-based encryption (ABE), and identity-based encryption (IBE). Additionally, hybrid clouds where sensitive data is housed in a private cloud are used to secure sensitive information. Knowledge extraction from the data and Privacy Preserving Data Publishing (PPDP) are integrated into the data processing step. To preserve data privacy, PPDP uses anonymization techniques including generalization and suppression. These processes may be further subdivided into methods based on association rule mining, classification, and clustering. Techniques based on association rule mining identify the relevant links and patterns in the input data, while clustering and classification divide the data into different categories. To manage a wide range of big data measures related to the 3 Vs, it is necessary to create effective frameworks that can analyze large amounts of data that are coming in at a fast pace from many sources. Big data must go through several stages in its life cycle 7.
Throughout the whole data lifecycle, big data privacy and security should be given top priority to ensure that the data are transferred and kept safely. But security and privacy ought to come first. Privacy is the right to maintain confidentiality and control over information disclosed to third parties. It is applicable to both individual and collaborative users 8. Security, on the other hand, is the act of employing technology, protocols, and training to guard against unauthorized access, disclosure, interruption, alteration, inspection, recording, and destruction of data and data assets 7. This paper will focus on how to ensure the privacy of the big data.
Two stages can be used to separate the data processing portion for privacy protection. Since the acquired data may contain sensitive information associated with the data owner, the first phase’s objective is to protect information from unwanted exposure. The objective of the second stage is to get valuable information from the data while maintaining privacy 7.
It is a method that can be a useful substitute for protecting privacy during data transfer and analysis. Current de-identification techniques, such as K-anonymity and its variations, and differential privacy-compliant procedures, protect data privacy while consuming very few resources 9.
The widely used k-anonymity methods usually suppress and generalize the quasi-identifier features that cause data leaks 10.
Generalization: As seen in Figure 6, the fundamental concept of generalization is that similar generalized values should be used instead of the quasi-identifier characteristics for the same equivalency category 10.
Suppression: This method may be thought of as a particular kind of generalization in which “*” is used instead of every value 10.
Figure 6: Example of fundamental concept of generalization 10.
Making a dataset k-anonymous (and perhaps l-diverse or t-close) is a difficult challenge. To preserve a client’s security, the original information is regarded to be delicate and private, and it consists of several records 10.
An attribute or group of attributes that can be used to identify a certain individual, such as a card number, ID, name ,and so on. These identification elements are often erased and encrypted before publication to safeguard the individual’s identity 10.
Quasi-identifier attributes is a collection of non-sensitive attributes in the information table that can be SQL-connected with an outer data database so that at least one individual can be recognized again, wherein any single attribute cannot identify unique people. Connecting a series of quasi-IDs might potentially differentiate the person’s characteristics. In a clinical security safeguarding situation, a group of semi-identifiers including age, gender, and postal code are stored in separate tables, and the patient's illness is considered a sensitive identifier 10.
Sensitive identifiers are fields that must be safeguarded, such as patient history or patient laboratory test results, information in medical data, employee pay, ID numbers, mobile phone numbers, and so on 10.
Insensitive attributes or non-sensitive attributes are those that, if published, will not breach the user’s Privacy of Big Data Protection. Non-sensitive attributes include all attributes that are not identifiers, quasi-identifiers, or sensitive attributes 10.
Figure 7: Example of k-anonymous dataset 10
Figure 8: Example of k-anonymous sensitive data 10
The quasi-identifiers, gender, age, and zip code are generalized and concealed here. These qualities in Figure 7 are regarded as quasi-identifiers since, when paired with other data, they show an individual’s identity. As a result, features such as age are generalized, as shown in Figure 6, while attributes such as zip code are suppressed, as shown in Figure 7 10.
Another method that is used to increase the privacy of big data is It is a type of group-based anonymization used to protect privacy in data sets by lowering the granularity of data representation. This drop is a compromise that results in some loss of viability of data management or mining algorithms for obtaining some privacy. The l-diversity model is an extension of the kanonymity model that reduces the granularity of data representation by using methods such as generalization and suppression in such a manner that each given record maps into at least k other records in the data 7.
It is an enhancement to l-diversity group-based anonymization, which is used to maintain privacy in data sets by reducing the level of detail of a data representation. This decrease is a trade-off that results in some loss of data management or mining algorithm adequacy to gain some privacy. The t-closeness model enhances the l-diversity model by handling attribute values differently by taking the distribution of data values for that attribute into consideration 7.
The methodology adopted in this study draws on methodological insights gleaned from seminal works in the field of data security governance and privacy challenges within the context of Saudi Arabia's big data landscape. Extensive insights from "Exploring Data Security Governance" by Sun, Zhang, and Fang (2021) and "Unraveling Privacy Challenges in Saudi Arabia's Big Data Landscape" by Alenezi and Alblwi (2020) provided a foundational understanding of the research domain.
The study's approach centered on a comprehensive literature review, synthesizing key concepts, frameworks, and challenges elucidated in prior research. Leveraging these methodological insights, a survey-based questionnaire was formulated to gather empirical data and insights from a diverse cohort within the Saudi Arabian big data ecosystem. The questionnaire, administered to 160 participants, aimed to capture multifaceted perspectives on privacy governance, regulatory awareness, perceived challenges, and recommended strategies within the realm of big data.
The triangulation of literature-based insights with empirical data obtained from the survey facilitated a holistic understanding of the current landscape of privacy governance in Saudi Arabia's burgeoning big data sphere. This methodological integration allowed for an in-depth exploration and analysis, enabling the formulation of nuanced recommendations and strategies to enhance privacy governance practices tailored to the specific nuances of the Saudi Arabian context.
Sun, Zhang, and Fang conducted an extensive inquiry into the intricacies of data security governance amidst the expansive realm of big data. Their methodological approach involved a meticulous examination of the existing challenges, status quo, and prospective strategies concerning data security governance.The foundation of their study was rooted in comprehending the multifaceted challenges prevailing in contemporary data security governance. They systematically analyzed the complexities arising from the convergence of massive datasets and the imperative need for robust governance measures. A pivotal component of their investigation revolved around delineating a comprehensive risk assessment framework tailored explicitly for the expanse of big data environments. This framework aimed at determinizing the various risks, vulnerabilities, and potential impacts entwined within the intricate fabric of expansive datasets. Central to their methodology was the emphasis on embedding governance principles seamlessly within the operational facets of big data. Sun, Zhang, and Fang underscored the significance of integrating robust governance frameworks as a linchpin for effective data security and privacy measures.The researchers adopted a multi-faceted approach encompassing extensive literature reviews, case analyses, and cross-comparative studies. This multifarious strategy allowed for a holistic understanding of the dynamic landscape of data security governance in the era of burgeoning big data.Their study also encompassed a cross-national comparative analysis, enabling a nuanced examination of governance approaches and challenges across diverse geographical domains. This comparative lens facilitated insights into varied regulatory frameworks and their efficacy concerning data security governance. The insights garnered from Sun, Zhang, and Fang's study hold significant relevance to the burgeoning big data landscape in the Kingdom of Saudi Arabia. Their comprehensive exploration of challenges and potential solutions offers invaluable guidance for enhancing privacy governance strategies within the Saudi context.
Alenezi and Alblwi embarked on a meticulous study aimed at unraveling the intricate web of privacy challenges intertwined with data security within the burgeoning big data landscape of Saudi Arabia. Their methodological approach entailed a comprehensive exploration, employing diverse strategies to delineate the unique facets of privacy governance within the Kingdom's context.The researchers commenced their investigation by meticulously identifying and dissecting the privacy challenges endemic to Saudi Arabia's big data ecosystem. Leveraging qualitative methods such as interviews and surveys, Alenezi and Alblwi unearthed the nuanced intricacies of data security concerns and privacy preservation challenges prevalent within the Kingdom. A distinctive facet of their research methodology was the in-depth exploration of how cultural norms and societal expectations within Saudi Arabia intersected with privacy concerns in the context of big data. This pivotal analysis shed light on the intricate interplay between cultural nuances and data privacy, offering insights into how these norms influence privacy governance strategies. Alenezi and Alblwi meticulously scrutinized the existing regulatory frameworks within Saudi Arabia pertinent to data privacy. Their study involved a comprehensive analysis of the efficacy of these regulations in addressing privacy concerns amidst the rapidly evolving landscape of big data applications in the Kingdom. Engaging with stakeholders across diverse sectors facilitated a nuanced understanding of multifaceted privacy challenges. The researchers adeptly amalgamated qualitative data from interviews and surveys with quantitative analyses, fostering a holistic comprehension of the complexities surrounding data security and privacy preservation. Drawing upon their methodological insights and findings, Alenezi and Alblwi formulated pertinent recommendations and implications for privacy governance tailored to the Saudi Arabian context. Their study culminated in actionable insights poised to inform policy frameworks and strategic initiatives addressing privacy concerns within the Kingdom's big data sphere.The methodological rigor employed by Alenezi and Alblwi in dissecting Saudi-specific privacy challenges offers invaluable insights for framing privacy governance strategies within the Kingdom. Their research significantly contributes to understanding the intricate interplay between cultural norms, regulatory frameworks, and privacy concerns, providing a robust foundation for crafting contextually relevant privacy policies.
Based on a study covering a sample of 160 people conducted to gauge perceptions and priorities regarding privacy governance within Saudi Arabia's big data landscape, the findings revealed noteworthy insights. The questionnaire comprised five key inquiries aimed at unraveling the intricate nuances of privacy governance challenges. Results indicated that 45% of participants advocated for high priority in allocating levels to privacy governance, while 30% favored moderate priority, with 15% expressing a preference for low priority, and 10% uncertain about their stance. In terms of the most challenging aspect of big data impacting privacy governance, 35% highlighted concerns regarding data processing and analytics, while 30% identified data collection and acquisition as pivotal challenges. The study revealed that pivotal measures deemed essential for fortifying privacy protection included implementing robust access controls (30%), regular privacy audits and assessments (25%), and stringent encryption techniques (20%), with 20% advocating for employing all mentioned measures. Notably, 40% of respondents affirmed being very aware of existing privacy regulations governing big data, with an additional 30% claiming a moderate level of awareness. Additionally, a considerable 35% emphasized insufficient data protection measures as the primary threat to data privacy, while 30% cited a lack of stringent regulatory enforcement, and 25% pointed to inadequate user awareness on privacy issues.
CONCLUSION:
In conclusion, this study offers invaluable insights into the realm of privacy governance within Saudi Arabia's burgeoning big data landscape. However, despite its contributions, several areas warrant attention for future enhancements and development.
Improvements and Future Directions:
This work lays the groundwork for future research endeavors aiming to fortify privacy governance strategies within Saudi Arabia's big data sphere. Firstly, further empirical studies with larger and more diverse participant samples could yield richer insights, ensuring comprehensive coverage of perspectives across various sectors. Additionally, conducting longitudinal studies would enable the tracking of evolving trends and attitudes towards privacy governance, offering a more nuanced understanding of the landscape's dynamics.
Moreover, while this study focused on methodological insights from seminal works and surveybased approaches, future research could explore diverse methodologies, including qualitative methods like in-depth interviews or focus groups. Such methods could provide deeper contextual insights into cultural norms and their interplay with privacy concerns within the Saudi context.
Limitations, Obstacles, and Challenges Encountered:
Despite its contributions, this research grappled with certain limitations and obstacles. One significant limitation lies in the inherent complexity of privacy governance within big data environments. The methodologies employed, though comprehensive, may not encapsulate the entirety of challenges or provide foolproof solutions due to the ever-evolving nature of data technologies and regulatory landscapes.
Additionally, the reliance on survey-based methodologies might have introduced response bias or limitations in the depth of responses, constraining the granularity of insights obtained. Cultural nuances and socio-economic factors influencing attitudes towards privacy might also pose challenges in capturing a comprehensive spectrum of perspectives.
Furthermore, the identified methods for enhancing privacy, such as k-anonymity and l-diversity, exhibit limitations in their efficacy and applicability within specific datasets or contexts. The compromise between privacy and utility poses a persistent challenge that necessitates continual refinement and adaptation.
Recommendations for Future Research and Implementation:
To address these limitations, future research endeavors should endeavor to embrace interdisciplinary approaches, integrating diverse methodologies and expertise from fields such as data science, law, sociology, and ethics. Collaboration between academia, industry, and regulatory bodies would foster a more holistic understanding and implementation of privacy governance strategies tailored to the Saudi big data landscape.
In the pursuit of mitigating challenges and refining privacy preservation methods, a continuous evaluation and adaptation of existing techniques are essential. New technological advancements and regulatory frameworks necessitate ongoing research and a proactive stance towards refining and developing novel methodologies to safeguard privacy within big data environments.
In essence, while this study sheds light on pivotal aspects of privacy governance in Saudi Arabia's big data realm, continual research, policy adaptations, and strategic frameworks are indispensable to navigate the ever-evolving intersection of big data, privacy, and security within the Kingdom.
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Received on 03.05.2025 Revised on 24.11.2025 Accepted on 21.03.2026 Published on 24.06.2026 Available online from June 30, 2026 International Journal of Technology. 2026; 16(1):1-10. DOI: 10.52711/2231-3915.2026.00001 ©A and V Publications All right reserved
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