Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 800-807· 0 citations
TL;DR
The integrated approach addresses the complex interplay between technical capabilities, organizational processes, and regulatory requirements necessary for sustainable privacy protection in distributed cloud computing architectures.
Abstract
The evolution of cloud computing environments has fundamentally transformed organizational approaches to data management and privacy protection, necessitating comprehensive infrastructure frameworks that integrate technical controls with organizational governance structures. Contemporary privacy compliance demands proactive implementation strategies that embed privacy considerations directly into cloud architecture design rather than treating compliance as supplementary functionality. The framework encompasses foundational elements including balanced technical-organizational control structures, multi-layered security architectures, and sophisticated integration approaches for legacy system compatibility. Privacy-by-design implementation requires systematic embedding of data protection requirements throughout development lifecycles, incorporating data minimization strategies, collection limitation principles, and user-centric control mechanisms. Technical infrastructure components include robust access control frameworks, advanced anonymization techniques, and comprehensive encryption strategies across all data states. Continuous monitoring capabilities enable real-time compliance oversight through sophisticated analytical frameworks, comprehensive audit trail mechanisms, and dynamic risk assessment methodologies that adapt to evolving cloud environments. The integrated approach addresses the complex interplay between technical capabilities, organizational processes, and regulatory requirements necessary for sustainable privacy protection in distributed cloud computing architectures.
Telecommunications organizations face mounting pressure to manage data infrastructure that is simultaneously scalable, cost-efficient, privacy-compliant, and secure. Existing literature addresses these dimensions in isolation, producing systems that satisfy one objective while degrading others. This paper proposes a unified co-design framework that treats privacy, cost, and security as first-order design constraints rather than sequential additions, applied specifically to cloud-native distributed data platforms in the telecommunications sector. The framework is instantiated through a metadata-parameterized pipeline architecture that enables configuration-driven ETL orchestration, partition-based distributed parallelism, and dynamic workflow routing. Six quantitative formulas are introduced to characterize system performance: a throughput model, a cost reduction index, a configuration reusability factor, a data exposure surface metric, a security coverage depth measure, and an identity governance completeness index. Evaluation against a representative telecommunications data environment demonstrates a pipeline throughput of 2.4 TB/hr at 87% parallel efficiency, a 34% reduction in cloud infrastructure cost relative to traditional deployment models, and a 41% reduction in data exposure surface following tiered privacy enforcement. Security coverage depth reached 0.94 across six independent security layers, and identity governance completeness attained 89% of managed lifecycle events. The framework is validated against GDPR Article 5(1)(c), CCPA Section 1798.100, CISA Zero Trust Maturity Model v2.0, and NIST IR 8505 guidance for cloud-native data protection. Findings indicate that co-design enables measurable, simultaneous improvement across all three constraint dimensions without the performance penalties characteristic of sequential integration approaches.
Suresh Tambe· International journal of com...· 0 citations
The study concludes that trustworthy digital participation depends on the integration of technical safeguards, enforceable rights, organisational culture, and transparent governance, and recommends embedding security and privacy by design, strengthening incident preparedness, improving workforce competence, enhancing regulatory cooperation, and adopting measurable accountability mechanisms.
Bisola Akeju, Shalom Alugwe, Ayokunle Olamide Ijagbemi· International Journal of Mul...· 0 citations
Smart ports increasingly rely on secure, interoperable, and scalable digital infrastructures to manage complex flows of cargo, vehicles, people, and information. Access management has therefore evolved from an operational security function into a strategic component of governance, cybersecurity, regulatory compliance, and logistics efficiency. This study proposes a cloud-enabled access management framework for smart port ecosystems. It adopts an exploratory, design-oriented methodology combining a Scopus-based literature review, benchmarking of 22 cloud-based access control systems, consultations with nine stakeholders, and value proposition analysis. The findings identify key technological, organizational, and regulatory requirements, including modular integration, multi-factor authentication, role-and zone-based access controls, interoperability with existing port systems, auditability, and compliance with data protection and information security standards. Stakeholder consultations further reveal recurring challenges, such as biometric reliability, entry delays, fragmented access control, high staff turnover, and continued reliance on manual procedures. The proposed framework links these challenges to value-creating mechanisms, including automated scheduling, remote management, API integration, alternative authentication methods, real-time monitoring, and non-compliance management. The study contributes to smart port research by conceptualizing cloud-enabled access management as both a governance and business model innovation capable of strengthening security, operational efficiency, and resilience across port ecosystems.
Unknown authors· COLLECTION OF PAPERS NEW ECO...· 0 citations
The growing adoption of data-driven decision-making has improved operational intelligence, predictive analytics, and strategic planning across enterprises. However, privacy regulations, data sovereignty requirements, and competitive concerns often restrict direct data sharing between organizations. Federated Data Engineering (FDE) addresses these challenges by enabling collaborative analytics without transferring sensitive raw data. This paper presents a privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics. The framework supports decentralized data preprocessing, feature engineering, and encrypted parameter sharing while complying with regulations such as GDPR and HIPAA. It incorporates privacy-enhancing technologies, including differential privacy, secure multi-party computation, homomorphic encryption, and blockchain-based auditing, to ensure confidentiality, integrity, and transparency. The proposed architecture also improves scalability, communication efficiency, fault tolerance, and interoperability across heterogeneous enterprise environments. Experimental evaluation demonstrates enhanced collaborative analytics with reduced privacy risks and communication overhead. The framework provides a practical foundation for secure, trustworthy, and privacy-preserving cross-enterprise data collaboration in healthcare, finance, manufacturing, and other data-intensive industries.
Karen Spärck Jones, Donald Michie· International Journal of Dat...· 0 citations
Cloud Computing has completely transformed the way businesses operate today with its scalability, flexibility, and cost efficiency. Cloud platforms have become a key component for organizations in various sectors to store data, deploy software, manage infrastructure, and implement digital transformation efforts. But the adoption of cloud computing has made it extremely challenging to deal with several security concerns like data breach, cyberattacks, unauthorized access, insider threats, and compliance concerns. The research paper employs secondary data collection and thematic analysis to critically discuss challenges of cloud security and risk management approaches in contemporary cloud computing environment. A thorough literature review of the scholarly papers from 2015 to 2023 is conducted to assess cybersecurity threats, cloud vulnerabilities, identity management systems, encryption technologies, compliance frameworks, and artificial intelligence-based threat detection systems.Peer reviewed journal articles, conference papers and industry reports were analysed using the method of themed analysis, which identified themes that appeared repeatedly throughout. The results show that cloud environments are still very susceptible to advanced cyberattacks, ranging from weak authentication and insecure APIs to misconfigured cloud services and inadequate governance. The study also points out the effectiveness of modern technologies like multi-factor authentication, zero-trust security models, artificial intelligence, and encryption enhance cloud security performance. But there are still issues in the way of the organizations' operations regarding regulatory compliance, data governance and shared security responsibilities.The study demonstrates that the key factors to achieving effective cloud security are continuous monitoring, robust governance policies, employee awareness training, and high-level cybersecurity frameworks. The study adds to the body of knowledge about the security of cloud computing, and offers practical suggestions for any organization working in the cloud.
Veeramani Sampathkumar, Dinesh Kumar Ramaraj, Rajesh Kotha et al.· International journal of com...· 0 citations
This research offers a pragmatic blueprint for digital transformation in resource-constrained settings, contributing to the discourse on leveraging data architectures for improved public service delivery and evidence-based policymaking.
