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Articles de revues sur le sujet « Ethical compliance Data privacy »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Smith, J. H., and JS Horne. "Data privacy and DNA data." IASSIST Quarterly 47, no. 3-4 (2023): 1–3. http://dx.doi.org/10.29173/iq1094.

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The letter to the Editor is in response to the manuscript by Hertzog et al. (2023) titled "Data management instruments to Protect the personal information of Children and Adolescents in sub-Saharan Africa." The letter elaborates on personal data protection, particularly the POPI Act's data management requirements; the DNA Act mandates specific measures to ensure the data integrity and security of the NFDD's information. In addition, it criminalises the misuse or compromise of the data's integrity within the NFDD. In addition, the DNA Act established the National Forensic Oversight and Ethical

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Pereira, Letícia de Mello, Maurício Alfredo Gewehr, and Marcia Fernanda Alves. "Enhancing Organizational Compliance Programs: The Impact of Data Protection Implementation under the Personal Data Protection Law." ESG Law Review 3, ssue (2020): e01612. http://dx.doi.org/10.37497/esg.v3issue.1612.

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This research aims to address the following question: Is there an intersection between compliance programs and the privacy requirements outlined in the General Data Protection Law (LGPD)? To achieve this objective, the study examines the key legal aspects that directly relate to compliance programs and need to be considered for effective implementation of data protection within a corporate organization, in accordance with Brazilian legislation. The research adopts a deductive method and relies on a thorough analysis of relevant literature. The findings of this study demonstrate that comp

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Chauhan, Pavitra, Lars Ailo Bongo, and Edvard Pedersen. "Ethical Challenges of Using Synthetic Data." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 133–34. http://dx.doi.org/10.1609/aaaiss.v1i1.27490.

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There is an outburst of digitized medical data with the growing adoption of Electronic Health Record (EHR) systems but have restricted access due to legal compliances. This lack of data accessibility has piqued the interest in generating and using synthetic data. Synthetic data is programmatically generated using the statistical properties of the real dataset. Although synthetic data tackles the issue of legal compliance, there are some ethical concerns associated with it. In this paper, we discuss three ethical concerns of synthetic medical data such as fairness, privacy and unwarranted use.

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Myeka, Pranith Kumar Reddy. "Data Governance and Privacy in Modern Database Architecture: A Comprehensive Analysis." European Journal of Computer Science and Information Technology 13, no. 20 (2025): 79–90. https://doi.org/10.37745/ejcsit.2013/vol13n207990.

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The rapid digital transformation has positioned data governance and privacy as critical priorities in modern database architecture. This article addresses the complex interplay between regulatory compliance, technical implementation, and ethical considerations in data management. Through comprehensive assessment of global privacy regulations like GDPR and CCPA, it demonstrates how organizations are adapting database architectures to meet evolving compliance requirements. The article evaluates the implementation of Role-Based Access Control (RBAC) systems, highlighting their effectiveness in ma

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Adedoyin Tolulope Oyewole, Bisola Beatrice Oguejiofor, Nkechi Emmanuella Eneh, Chidiogo Uzoamaka Akpuokwe, and Seun Solomon Bakare. "DATA PRIVACY LAWS AND THEIR IMPACT ON FINANCIAL TECHNOLOGY COMPANIES: A REVIEW." Computer Science & IT Research Journal 5, no. 3 (2024): 628–50. http://dx.doi.org/10.51594/csitrj.v5i3.911.

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In an era where the digital transformation of financial services is both a boon and a battleground, this paper meticulously navigates the intricate relationship between Financial Technology (FinTech) and the evolving landscape of data privacy laws. With the digital economy's expansion, FinTech companies stand at the forefront of innovation, offering unprecedented financial inclusion and efficiency opportunities. However, this rapid advancement also raises significant concerns regarding data privacy and consumer protection, necessitating a delicate balance between innovation and compliance. Thi

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Raj, Diana Judith Irudaya, Vijay Sai Radhakrishnan, Manyam Rajasekhar Reddy, Natarajan Senthil Selvan, Balasubramanian Elangovan, and Manikandan Ganesan. "The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15969–74. http://dx.doi.org/10.48084/etasr.7969.

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Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a bal

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Grace Annie Chintoh, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Amazing Hope Ekeh. "Cross-Jurisdictional data privacy compliance in the U.S.: developing a new model for managing AI data across state and federal laws." Gulf Journal of Advance Business Research 3, no. 2 (2025): 537–48. https://doi.org/10.51594/gjabr.v3i2.96.

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The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardize

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Researcher. "PRIVACY-CENTRIC DATA WAREHOUSING IN MARKETING: NAVIGATING THE INTERSECTION OF ANALYTICS AND REGULATORY COMPLIANCE." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 982–99. https://doi.org/10.5281/zenodo.14055386.

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In the era of data-driven marketing, organizations face the dual challenge of leveraging vast amounts of consumer data for analytics while adhering to increasingly stringent privacy regulations. This article examines the concept of privacy-first data warehouses as a solution to balance marketing effectiveness with regulatory compliance. We explore the evolution of data warehouses in marketing, analyze the impact of regulations such as GDPR and CCPA, and propose a framework for implementing privacy-centric data infrastructures. Through a combination of technical strategies, including data minim

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Olagunju, Funminiyi. "Federated Learning in the Era of Data Privacy: An Exhaustive Survey of Privacy Preserving Techniques, Legal Frameworks, and Ethical Considerations." International Journal of Future Engineering Innovations 2, no. 3 (2025): 153–60. https://doi.org/10.54660/ijfei.2025.2.3.153-160.

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Federated Learning (FL) has emerged as a transformative approach to decentralized machine learning, enabling model training across multiple devices without centralizing sensitive data. While FL inherently supports privacy, growing concerns around data security, regulatory compliance, and ethical accountability have led to the development of advanced privacy preserving mechanisms. This systematic review, conducted in adherence with PRISMA guidelines, explores the landscape of privacy enhancing techniques, legal regulations, and ethical implications associated with Federated Learning. We sourced

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Grace Annie Chintoh, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Amazing Hope Ekeh. "Developing a conceptual framework for U.S. data privacy compliance in AI systems: Integrating CCPA and HIPAA Regulations." International Journal of Frontline Research and Reviews 4, no. 1 (2025): 011–19. https://doi.org/10.56355/ijfrr.2025.4.1.0034.

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The rapid adoption of artificial intelligence (AI) across industries has heightened the importance of robust data privacy compliance, particularly in the U.S., where complex regulatory frameworks such as the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA) govern data usage. This paper proposes a conceptual framework to harmonize these regulations within AI system design, emphasizing transparency, accountability, and ethical governance principles. The framework addresses key challenges, including regulatory gaps, legal risks, and ethica

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Lokeshkumar Madabathula. "Ethical Considerations in Advanced Data Analytics : Balancing Innovation and Privacy." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 416–22. https://doi.org/10.32628/cseit25112374.

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The evolving landscape of data analytics presents organizations with dual challenges of maximizing innovation while maintaining ethical responsibilities. This article explores the complex relationship between technological advancement and ethical data management, examining key areas including privacy preservation, algorithmic fairness, and regulatory compliance. Through analysis of real-world implementations across healthcare, urban planning, and enterprise sectors, the article presents evidence-based frameworks for ethical data governance. The article highlights the critical role of data prof

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Dr. Rachna Thakkar. "Data Privacy and Consumer Trust in Social Media Marketing." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 494–99. https://doi.org/10.47392/irjaem.2025.0078.

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In today’s digital landscape, social media marketing is pivotal in consumer engagement and brand outreach. However, the increasing concern over data privacy has raised significant ethical and regulatory issues. Companies leverage vast amounts of consumer data to personalize advertisements, but unauthorized data collection, lack of transparency, and frequent data breaches have eroded consumer trust (Smith et al., 2021; Jones & Patel, 2020). This research investigates the relationship between data privacy practices and consumer trust, analyzing key factors such as regulatory compliance, ethi

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Myint, Yin Lei Yee, Rajermani Thinakaran, Hushalictmy Paliyanny, Kaung Khant Yan Naing, and Saule Kumargazhanova. "Ensuring the ethical application of user data in IoE-Driven E-commerce: A systematic review with proposed framework." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 899–908. https://doi.org/10.53894/ijirss.v8i3.6662.

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The purpose of the study is to propose a framework to ensure the ethical application of user data on the Internet of Everything (IoE) commercial platforms by investigating the ethical application of user data on IoE-driven e-commerce platforms, focusing on privacy challenges, regulatory impacts, and innovative privacy-preserving techniques. A systematic literature review (SLR) methodology was employed to analyze existing research on privacy challenges, regulatory frameworks, and technological solutions within IoE-driven e-commerce. The study synthesizes findings from 22 scholarly articles acro

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Parks, Cecelia. "Beyond Compliance: Students and FERPA in the Age of Big Data." Journal of Intellectual Freedom and Privacy 2, no. 2 (2017): 23. http://dx.doi.org/10.5860/jifp.v2i2.6253.

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Privacy is governed by an array of laws in the United States, and this paper examines one facet of privacy regulation: the privacy of students’ academic records. The Family Educational Rights and Privacy Act (FERPA) protects the privacy of these records, but how do students understand their rights under FERPA, especially with the development of big-data and learning-analytics technologies that demand unprecedented sharing of student data? This paper begins to answer that question by examining existing literature on privacy in general and with regards to FERPA specifically. It suggests that FER

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Bhanu Teja Reddy Maryala. "The role of synthetic data in governance: Frameworks for ethical implementation and regulatory compliance." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4462–68. https://doi.org/10.30574/wjarr.2025.26.2.2046.

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Synthetic data has emerged as a transformative resource in artificial intelligence development, offering compelling solutions to longstanding challenges in data privacy, accessibility, and representational equity. This article examines the governance dimensions of synthetic data deployment, with particular attention to emerging risks including algorithmically hallucinated content, unintentional privacy leakages, and potential regulatory circumvention. Despite significant adoption growth across regulated industries, substantial governance gaps persist, with many organizations lacking formal fra

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Rohini Isarapu and Sharathchandra Gowda. "Data Privacy in the Age of Cloud: Ethical Considerations and Impacts on Society." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2174–80. https://doi.org/10.32628/cseit2410612421.

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The widespread adoption of cloud computing has fundamentally transformed how organizations manage and protect data, introducing complex privacy challenges alongside unprecedented opportunities. This comprehensive article explores the ethical considerations and societal impacts of data privacy in cloud environments, examining how organizations navigate consent mechanisms, data ownership, and regulatory compliance. The article investigates the evolution of data collection practices, the implementation of privacy frameworks, and the critical role of corporate responsibility in maintaining stakeho

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Chiranjeevi, Bura, Kamatala Srikanth, and Kumar Myakala Praveen. "Ethical Challenges in Data Science: Navigating the Complex Landscape of Responsibility and Fairness." International Journal of Current Science Research and Review 08, no. 03 (2025): 1067–78. https://doi.org/10.5281/zenodo.14986766.

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Abstract : The rapid advancement of data science and artificial intelligence (AI) has revolutionized decision-making across multiple domains, including healthcare, finance, and law enforcement. However, these advancements come with pressing ethical challenges, such as algorithmic bias, data privacy risks, and lack of transparency. This paper systematically analyzes these ethical concerns, focusing on state-of-the-art methodologies for bias detection, explainable AI (XAI), and privacy-preserving techniques. We provide a comparative evaluation of ethical frameworks, including the ACM Code of Eth

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Mandayam, Rashmi. "AI and Personal Data: The Art of Balancing Privacy." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42363.

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The rapid evolution of artificial intelligence (AI) has transformed how personal data is harnessed, driving innovation in diverse fields, from healthcare to finance. However, this transformation brings complex challenges in safeguarding individual privacy while leveraging vast amounts of personal information. This paper examines the dual role of AI as both a catalyst for technological advancement and a potential risk to privacy. By analyzing current trends in AI data usage, exploring the inherent challenges in balancing privacy and functionality, and highlighting emerging solutions—such as fed

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Adithya Jakkaraju, Venugopal Muraleedharan Mini. "Ethical Synthetic Data Generation via Fairness-Aware Generative Models." Journal of Information Systems Engineering and Management 10, no. 24s (2025): 740–52. https://doi.org/10.52783/jisem.v10i24s.3988.

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Synthetic data has emerged as a crucial component in AI model training, offering privacy protection and enhanced data diversity. However, generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often inherit and amplify biases present in training datasets, leading to ethical concerns. This paper explores fairness-aware generative models that embed fairness constraints (e.g., demographic parity, equalized odds) to mitigate bias during data synthesis. We review methods for bias quantification in synthetic data, regulatory compliance frameworks, and al

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Ugochukwu Francis Ikwuanusi, Peter Adeyemo Adepoju, and Chinekwu Somtochukwu Odionu. "https://orionjournals.com/ijmru/ArchiveIssue-2023-Vol6-Issue2." International Journal of Multidisciplinary Research Updates 6, no. 2 (2023): 033–44. https://doi.org/10.53430/ijmru.2023.6.1.0063.

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As libraries increasingly integrate Artificial Intelligence (AI) to enhance operations and user experiences, data privacy has emerged as a critical concern. Libraries collect vast amounts of user data, including borrowing histories, digital interactions, and demographic information, making them susceptible to privacy risks such as unauthorized access, data breaches, and algorithmic profiling. This study investigates the role of ethical AI practices in addressing these data privacy issues, ensuring trust, transparency, and compliance with global privacy standards. Ethical AI emphasizes principl

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Naik, Arjun. "Influence of AI in Banking: Ethical and Compliance Implications." Journal of Global Research in Computer Sciences 15, no. 1 (2024): 8. https://doi.org/10.4172/2229-371X.15.2.002.

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Artificial Intelligence (AI) is revolutionizing the banking industry, presenting possibilities for extended efficiency, personalized services and risk control. However, as AI era is increasingly growing into banking operations, ethical troubles and compliance issues are rising as critical concerns in this zone. This article explores the effect of AI in banking, analyzing both positive and negative characteristics. It delves into the ethical and compliance concerns surrounding the integration of AI in financial establishments. We explore how AI is remodeling banking operations, consisting of cu

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Pratomo, Arief Budi, Joko Santoso, Anggun Nugroho, Rully Fildansyah, and Arnes Yuli Vandika. "Analysis of Data Privacy Policy, Data Processing Ethics, and Technology Ethics Awareness on User Privacy Protection in West Java." West Science Social and Humanities Studies 2, no. 03 (2024): 412–22. http://dx.doi.org/10.58812/wsshs.v2i03.715.

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This study investigates the relationships between data privacy policy adherence, data processing ethics, technological ethics awareness, and user privacy protection in West Java, Indonesia. Utilizing a quantitative research design, data was collected through surveys administered to organizations and individual users. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) analysis was employed to analyze the data. The results reveal significant positive relationships between data privacy policy adherence, data processing ethics, and technological ethics awareness with user privacy

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Mamakou, Xenia J., Dimitris K. Kardaras, and Eleutherios A. Papathanassiou. "Evaluation of websites’ compliance to legal and ethical guidelines: A fuzzy logic–based methodology." Journal of Information Science 44, no. 4 (2017): 425–42. http://dx.doi.org/10.1177/0165551517697610.

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Privacy issues are a top priority in web design. However, websites’ evaluation methods do not consider legal and ethical issues. This article proposes a fuzzy logic–based methodology for evaluating websites’ compliance with legal and ethical principles. Using fuzzy Delphi and fuzzy numbers, the methodology develops the Fuzzy Legal and Ethical Compliance Index (FLECI) that addresses the inherited vagueness of the evaluation process and calculates websites’ conformity to legal and ethical guidelines. To illustrate the proposed methodology, this research collects data and then evaluates and class

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Rachapalli, Sai Kalyani. "Building AI Pipelines That Comply with GDPR, HIPAA, and Industry Standards." International Scientific Journal of Engineering and Management 01, no. 01 (2022): 1–9. https://doi.org/10.55041/isjem00141.

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The high take-up rate of artificial intelligence (AI) by different sectors has triggered critical interest in data security, privacy, and ethics compliance. This work discusses the design and process blueprint for the establishment of AI pipelines that comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and different sectoral standards. The aim is to offer a sound methodology to incorporate compliance checks at every stage of the AI lifecycle, from data collection to deployment. Our method focuses on incorporating privacy-

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Grace Annie Chintoh, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Amazing Hope Ekeh. "The role of AI in U.S. consumer privacy: Developing new concepts for CCPA and GLBA compliance in smart services." Gulf Journal of Advance Business Research 3, no. 2 (2025): 549–60. https://doi.org/10.51594/gjabr.v3i2.97.

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The rapid adoption of artificial intelligence (AI) in U.S. consumer services has transformed customer interactions, operational efficiency, and service delivery. However, this technological shift presents complex challenges in maintaining compliance with data privacy regulations, such as the California Consumer Privacy Act (CCPA) and the Gramm-Leach-Bliley Act (GLBA). This paper explores the role of AI in enhancing smart services while safeguarding consumer privacy, highlighting key risks, compliance challenges, and regulatory gaps. A conceptual model is proposed to guide organizations in inte

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Dhruvitkumar V Talati. "Enhancing data security and regulatory compliance in AI-driven cloud ecosystems: Strategies for advanced information governance." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 579–94. https://doi.org/10.30574/wjarr.2022.15.3.0905.

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This study examines adaptive information governance models to address the key issues of AI-based cloud environments, in the end aiming to enable enhanced data security and regulatory compliance. Conventional governance models fail to respond to complexity issues posed by AI-cloud integration, with this resulting in incident response shortcomings, privacy laws, and regulatory compliance identification. In response to these weaknesses, this research analyzes governance elements such as Privacy-Enhancing Technologies (PETs), ethical regulation, and incident response models using sophisticated qua

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Siva Prasad Sunkara. "AI-Driven Enterprise Cloud Solutions: Balancing Innovation, Privacy, and Ethics for Sustainable Business Transformation and Stakeholder Trust." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 529–37. https://doi.org/10.32996/jcsts.2025.7.2.56.

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This article explores the complex interplay between artificial intelligence, data privacy, and ethical considerations within enterprise cloud solutions. As organizations increasingly adopt AI-powered CRM, ERP, and automation systems, they face the dual challenge of driving business innovation while safeguarding societal trust. The discussion examines how companies can implement enhanced data privacy compliance mechanisms, detect and mitigate algorithmic bias, and establish robust governance frameworks. By analyzing current regulatory landscapes, technical solutions, and organizational strategi

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Yadav, DR T. Chandrasekhar, Kasturi Kala, Raja Ishwarya Roy Kolachina, Mourya Chandra Kanneganti, and Shanmukha Sai Pasupuleti. "Data Privacy Concerns and their Impact on Consumer Trust in Digital Marketing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem38555.

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The rise of digital marketing has transformed consumer-brand interactions through personalized strategies and targeted ads driven by extensive consumer data. However, increased data collection has sparked privacy concerns, affecting consumer trust. This study examines how dataprivacy concerns impact consumer trust, engagement, and willingness to share information in digital marketing. Key privacy issues include over-collection, lack of transparency, unauthorized sharing, and data breaches. Using a mixed-methods approach, with quantitative surveys of 600 participants and qualitative interviews

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B T, Dr Chitra. "Privacy and Innovation in IoT: Legal, Ethical, and Entrepreneurial Perspectives." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 164–74. https://doi.org/10.22214/ijraset.2025.72885.

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Privacy is a major concern in the era of connected technologies, especially with the exponential growth of Internet of Things (IoT) devices that collect sensitive personal data. This paper, titled Privacy Protection in IoT Data Collection, explores how entrepreneurs can address these concerns through privacy-preserving technologies, compliance with national and international laws, and the strategic use of intellectual property rights (IPR). Ensuring the privacy and security of this data is paramount to building trust and encouraging widespread adoption of IoT technologies. The paper examines v

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Researcher. "ETHICAL AI IN BANKING: BALANCING INNOVATION WITH RESPONSIBILITY." International Journal of Artificial Intelligence & Machine Learning (IJAIML) 3, no. 2 (2024): 95–100. https://doi.org/10.5281/zenodo.13627526.

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The integration of Artificial Intelligence (AI) in banking promises enhanced efficiency, personalized services, and innovative solutions. However, the deployment of AI in this sector is fraught with ethical challenges, including bias, transparency, accountability, and data privacy concerns. This paper explores the pitfalls associated with implementing AI-based solutions in banking and proposes strategies for integrating ethical considerations to mitigate these challenges. Through examining real-world case studies and existing frameworks, we provide recommendations for banking institutions to a

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Obudho, Kotch. "The Impact of Data Privacy Laws on Digital Marketing Practices." Journal of Modern Law and Policy 4, no. 1 (2024): 35–48. http://dx.doi.org/10.47941/jmlp.2155.

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Purpose: The general study focused on investigating the impact of data privacy laws on digital marketing practices. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secon

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Oladipupo, Dopamu, Adesiyan Joseph, and Oke Femi. "Artificial intelligence and US financial institutions: Review of AI-assisted regulatory compliance for cybersecurity." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 964–79. https://doi.org/10.5281/zenodo.14062809.

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As cyber threats and regulations become increasingly complex, financial institutions in the U.S. are in need of innovative cyber security solutions. This study examines the potential for artificial intelligence (AI) in addressing this problem. Artificial intelligence has significant potential for real-time threat detection, automated compliance processes, and proactive risk management. Nonetheless, ethical considerations, concerns about personal data privacy, and potential biases in AI algorithms require careful consideration. In light of this, the research proposes recommendations for develop

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Chintoh, Grace Annie, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Amazing Hope Ekeh. "Proposing a Data Privacy Impact Assessment (DPIA) Model for AI Projects under U.S. Privacy Regulations." International Journal of Social Science Exceptional Research 3, no. 1 (2024): 95–102. https://doi.org/10.54660/ijsser.2024.3.1.95-102.

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The rapid adoption of artificial intelligence (AI) across industries such as healthcare, finance, and technology has amplified concerns about data privacy and regulatory compliance. Current methodologies for conducting Data Privacy Impact Assessments (DPIAs) often fail to address the unique challenges AI systems pose, including algorithmic bias, data diversity, and opacity. This paper proposes a tailored DPIA model designed to navigate the complexities of AI projects under U.S. privacy regulations, including CCPA, HIPAA, and GLBA. The model integrates key components such as risk identification

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Salako, Ademola Oluwaseun, Jumai Adedoja Fabuyi, Nsidibe Taiwo Aideyan, Oluwatosin Selesi-Aina, Dooshima Louisa Dapo-Oyewole, and Oluwaseun Oladeji Olaniyi. "Advancing Information Governance in AI-Driven Cloud Ecosystem: Strategies for Enhancing Data Security and Meeting Regulatory Compliance." Asian Journal of Research in Computer Science 17, no. 12 (2024): 66–88. https://doi.org/10.9734/ajrcos/2024/v17i12530.

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This study explores adaptive information governance models to address critical challenges in AI-driven cloud environments, focusing on enhancing data security and achieving regulatory compliance. Existing frameworks often fail to account for the complexities introduced by AI and cloud integration, leaving significant gaps in incident response, privacy protection, and governance practices. To bridge these gaps, this research evaluates governance components—Privacy-Enhancing Technologies (PETs), ethical oversight, and incident response metrics—through advanced quantitative methods, including Str

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Pokhidnia, Bohdan. "ETHICS IN INFORMATION MANAGEMENT: PERSONAL DATA PROTECTION." Economic scope, no. 197 (February 11, 2025): 212–16. https://doi.org/10.30838/ep.197.212-216.

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This article addresses the ethical dimensions of information management with a focus on personal data protection. Rapid digitalization has heightened the need for secure data handling and brought to light ethical dilemmas surrounding data privacy. The study identifies key challenges including cybersecurity threats, data breaches, and the ethical obligations of corporations to safeguard personal information in compliance with international regulations. Major international data protection standards (e.g., GDPR, ISO/IEC 27001) are evaluated, with particular attention to how corporate ethics foste

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Pereira, Letícia de Mello, Maurício Alfredo Gewehr, and Marcia Fernanda Alves. "Intersection between Compliance Programs and Privacy: Examining Data Protection under the General Data Protection Law (LGPD) in Corporate Organizations." Journal of Law and Corruption Review 3, ssue (2021): e063. http://dx.doi.org/10.37497/corruptionreview.3.2021.63.

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The main objective of this research is to answer the following question: Is there an intersection between compliance programs and the privacy sought through the General Data Protection Law (LGPD)? To achieve this, the main legal aspects that directly relate to compliance programs and need to be observed for the effective implementation of data protection under Brazilian legislation within a corporate organization will be outlined. Thus, a deductive method will be used through bibliographic analysis. It is concluded that considering that compliance programs are the means by which a company comm

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Tanvir Rahman Akash, Nusrat Jahan Sany, Lamia Akter, and Sanjida Akter Sarna. "Privacy - Preserving Technique in cybersecurity: Balancing Data Protection and User Rights." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 248–63. https://doi.org/10.32996/jcsts.2025.7.3.90.

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Increasing technological complexity of cyber threats creates a major challenge between securing data privacy and maintaining potent cybersecurity practices. The paper examines privacy-protecting security methods in cybersecurity by detailing organizational approaches to defend private information throughout the cyber threat detection and mitigation process. Organizations need to establish the appropriate levels of data security because implementations that limit privacy too much threaten their security capabilities but weak protection measures create vulnerabilities to data breaches. The resea

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Vijayan, Naveen Edapurath. "Privacy-Preserving Analytics in HR Tech- Federated Learning and Differential Privacy Techniques for Sensitive Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem11473.

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This paper explores the application of privacy-preserving analytics in human resources (HR), focusing on the synergistic use of federated learning and differential privacy. As HR departments increasingly leverage data-driven insights, the protection of sensitive employee information becomes paramount. Federated learning enables collaborative model training without centralizing raw data, while differential privacy adds calibrated noise to ensure individual data remains indiscernible. Together, these techniques form a robust framework for safeguarding HR data while enabling advanced analytics. T

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Saha, Rudrani. "Data Privacy and Cyber Security in Digital Library Perspective: Safe Guarding User Information Rudrani Saha." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30761.

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In an era the security is very much important/essential for information and knowledge. As Knowledge/information enlarging the need to arrange it and to provide sufficient security become more processing. These study measure libraries to follow different standards, technology and rules to protect user data and ensure privacy when accessing e – resources and other information. Also discussing the importance of cyber security in the digital library landscape. Also different challenges associated with safe guarding sensitive information within library e - resources. It explores various dimensions

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Zhai, Quanquan. "Technology-Driven Legal Innovation: The Role of Smart Legal Solutions in Corporate Compliance and Governance." International Journal of Law, Ethics and Social Sciences 1, no. 1 (2023): 1–12. http://dx.doi.org/10.70088/en5t5t57.

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With the rapid development of technologies such as artificial intelligence, big data, and blockchain, smart legal solutions are becoming essential tools for corporate compliance and governance management. These technology-driven legal solutions help organizations streamline operations, improve data analysis, and enhance compliance through automation, enabling businesses to operate efficiently in complex legal environments. This paper explores the applications of smart legal solutions in corporate compliance, contract management, risk management, and internal governance, while analyzing ethical

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Chen, Xiuli, and Joohan Ryoo. "Improving Ethical Leadership in Sustainable Public Health Through Fractal AI." European Journal of Applied Science, Engineering and Technology 3, no. 1 (2025): 43–61. https://doi.org/10.59324/ejaset.2025.3(1).04.

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This study explores innovative, ethical leadership approaches using artificial intelligence (AI) and fractal geometry in public health while fostering sustainable business practices within public health systems. The research employs a qualitative methodology based on case studies, secondary data analysis, and fractal-based AI algorithm evaluations. It examines advanced algorithms' technical applications in public health settings, improving data privacy, copyright, and intellectual property protection. The study finds that fractal algorithms offer robust solutions for promoting ethical leadersh

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Dean, Matthew D., Dinah M. Payne, and Brett J. L. Landry. "Data mining: an ethical baseline for online privacy policies." Journal of Enterprise Information Management 29, no. 4 (2016): 482–504. http://dx.doi.org/10.1108/jeim-04-2014-0040.

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Purpose – The purpose of this paper is to advocate for and provide guidance for the development of a code of ethical conduct surrounding online privacy policies, including those concerning data mining. The hope is that this research generates thoughtful discussion on the issue of how to make data mining more effective for the business stakeholder while at the same time making it a process done in an ethical way that remains effective for the consumer. The recognition of the privacy rights of data mining subjects is paramount within this discussion. Design/methodology/approach – The authors der

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Choon, Keong Tan1* Honggang Shi2. "Guidelines on The Ethical Use of ChatGPT or Baidu Among University Students in Xinjiang, China." UAI Journal of Arts, Humanities and Social Sciences (UAIJAHSS) 1, no. 5 (2024): 1–8. https://doi.org/10.5281/zenodo.14252791.

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<em>With an emphasis on important ethical aspects, this study investigates the connection between students' academic achievement and the Artificial Intelligence (AI) Ethics and Compliance Model. It's crucial to comprehend how AI technologies like ChatGPT and Baidu (China’s version of ChatGPT) conform to ethical standards given their growing usage in educational settings. Student privacy, data governance, fairness, accountability, transparency, explainability, and reproducibility are among the dimensions of the AI Ethics and Compliance Model that are being looked at. These dimensions were

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Geraldine O. Mbah. "Data privacy and the right to be forgotten." World Journal of Advanced Research and Reviews 16, no. 2 (2022): 1216–32. https://doi.org/10.30574/wjarr.2022.16.2.1079.

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In the digital era, data privacy has become a critical issue as vast amounts of personal information are collected, processed, and stored by corporations, governments, and online platforms. The growing reliance on data-driven technologies, including artificial intelligence and big data analytics, has heightened concerns over the security and ethical handling of personal data. Amid these concerns, the Right to Be Forgotten (RTBF) has emerged as a legal and ethical concept aimed at granting individuals’ greater control over their digital footprint. This right, enshrined in the European Union’s G

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Sunday Adeola Oladosu, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Adeoye Idowu Afolabi, Adebimpe Bolatito Ige, and Olukunle Oladipupo Amoo. "Frameworks for ethical data governance in machine learning: Privacy, fairness, and business optimization." Magna Scientia Advanced Research and Reviews 7, no. 2 (2023): 096–106. https://doi.org/10.30574/msarr.2023.7.2.0043.

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The rapid growth of machine learning (ML) technologies has transformed industries by enabling data-driven decision-making, yet it has also raised critical ethical concerns. Frameworks for ethical data governance are essential to ensure that ML systems uphold privacy, fairness, and business optimization while addressing societal and organizational needs. This review explores the intersection of these three pillars, providing a structured approach to balance competing priorities in ML applications. Privacy concerns focus on safeguarding individuals' data through strategies such as anonymization,

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Collins, Anuoluwapo, Oladimeji Hamza, Adeoluwa Eweje, and Gideon Opeyemi Babatunde. "Challenges and Solutions in Data Governance and Privacy: A Conceptual Model for Telecom and Business Intelligence Systems." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1064–81. https://doi.org/10.54660/.ijmrge.2024.5.1.1064-1081.

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This paper explores the challenges and solutions in data governance and privacy within the context of telecommunications and business intelligence (BI) systems, with a specific focus on the impact of 5G technology. As 5G networks become increasingly integral to the digital transformation of industries, the volume, variety, and velocity of data generated pose significant concerns regarding data security, privacy, and regulatory compliance. With the interconnection of billions of devices and the shift to software-defined networks, telecom operators face the dual challenge of optimizing network p

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Dheerendra, Yaganti. "Designing Privacy-First Health Monitoring Architectures Using Federated AI and FHIR-Compliant Azure Health Data Services In .NET." Journal of Scientific and Engineering Research 11, no. 8 (2024): 215–20. https://doi.org/10.5281/zenodo.15241066.

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The rapid advancement of digital health technologies necessitates architectures that ensure data privacy, regulatory compliance, and intelligent analytics. This paper presents a privacy-first health monitoring system that integrates Federated Artificial Intelligence (AI), Fast Healthcare Interoperability Resources (FHIR), and Azure Health Data Services within a .NET framework. The proposed architecture enables real-time health data collection and decentralized machine learning while preserving patient privacy and meeting HIPAA and FHIR compliance standards. By leveraging federated learning, th

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Abdullah, Musrizal, Teuku Zulfikar, and Sehat Ihsan Shadiqin. "MANAJEMEN DATA AKADEMIK PERGURUAN TINGGI KEAGAMAAN ISLAM SWASTA (STUDI LITERATURE REVIEW)." An-Nadzir : Jurnal Manajemen Pendidikan Islam 2, no. 01 (2024): 48–59. http://dx.doi.org/10.55799/annadzir.v2i01.356.

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Data management also has a key role in ensuring institutional transparency and accountability. With accurate and reliable academic reporting, Islamic Higher Education can build trust in the community, prospective students, and other stakeholders. The method used in this research is the SLR (Systematic Literature Review) approach. By applying this strategy, researchers identify, analyze, evaluate, and interpret all research relevant to the chosen topic. The results of this research are that academic data management must comply with applicable legal regulations, especially in terms of data prote

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Devarasetty, Narendra. "AI-Driven Data Governance Frameworks for Enhanced Privacy and Compliance." International Journal of Scientific Research and Management (IJSRM) 11, no. 02 (2023): 983–1006. https://doi.org/10.18535/ijsrm/v11i02.ec4.

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While data is growing at an unprecedented rate and, at the same time, the necessary privacy standards are tightening, traditional approaches to data management no longer prove effective. The robust and integrated approach of AI-driven data governance presents a further opportunity to optimize some crucial processes, work in line with real-time regulation, and improve the data privacy measures. In this article, the author aims at presenting a broader view of how AI can be incorporated into the overall context of data governance, with an emphasis on automated classification of data, and anomalou

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Arenike Patricia Adekugbe and Chidera Victoria Ibeh. "NAVIGATING ETHICAL CHALLENGES IN DATA MANAGEMENT FOR U.S. PROGRAM DEVELOPMENT: BEST PRACTICES AND RECOMMENDATIONS." International Journal of Management & Entrepreneurship Research 6, no. 4 (2024): 1023–33. http://dx.doi.org/10.51594/ijmer.v6i4.982.

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In the landscape of U.S. program development, the ethical management of data plays a crucial role in ensuring the integrity, privacy, and trustworthiness of information. This review outlines best practices and recommendations for navigating ethical challenges inherent in data management within this context. Understanding ethical challenges involves recognizing the complexities of data collection, storage, usage, and sharing, and the potential dilemmas they pose. Best practices entail implementing robust procedures for informed consent, privacy protection, encryption, access control, and compli

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