Privacy-Preserving Computing Models for Future Digital Ecosystems

Tuomas Aura

Privacy-Preserving Computing Models for Future Digital Ecosystems

Author(s)	Tuomas Aura
Country	United States
Abstract	The rapid expansion of digital ecosystems has resulted in the generation, exchange, and processing of massive volumes of sensitive information across interconnected platforms. While advanced technologies such as artificial intelligence, cloud computing, Internet of Things, and distributed systems have improved digital capabilities, they have also increased concerns related to data privacy, unauthorized access, and information misuse. Traditional data protection approaches often rely on centralized security mechanisms, which may not provide sufficient protection in complex and highly connected digital environments. Privacy-preserving computing models have emerged as innovative approaches that enable secure data processing while maintaining confidentiality and user privacy. This study examines privacy-preserving computing models for future digital ecosystems by analysing their contribution to secure information processing, privacy protection, data security, and trusted digital interactions. The research explores advanced privacy-enhancing approaches including secure multi-party computation, federated learning, homomorphic encryption, differential privacy, and trusted execution environments. A simulation-based analytical framework is developed to compare conventional computing approaches with privacy-preserving models using evaluation indicators including privacy protection effectiveness, security performance, computational efficiency, and data reliability. The findings demonstrate that privacy-preserving computing models provide significant improvements in protecting sensitive information while enabling secure data utilization. The results indicate that privacy-aware approaches create a balance between data accessibility and confidentiality, allowing organizations to extract value from information without compromising individual privacy. However, successful implementation requires addressing challenges related to computational complexity, scalability, regulatory compliance, interoperability, and practical adoption.
Keywords	Privacy-Preserving Computing, Digital Ecosystems, Data Privacy, Secure Computation, Federated Learning, Homomorphic Encryption, Cybersecurity
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-20

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American International Journal of Research and Innovation

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Privacy-Preserving Computing Models for Future Digital Ecosystems

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