1 Department of Computer Science Engineering, Government Polytechnic, Hosadurga, Chitradurga, Karnataka, India.
2 Department of Computer Science and Engineering Government Polytechnic Turuvekere, Tumkur, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2020, 06(02), 275-280
Article DOI: 10.30574/wjarr.2020.6.2.0125
Received on 10 may 2020; revised on 25 May 2020; accepted on 28 May 2020
Data privacy and security in heterogeneous edge-to-cloud architectures have become increasingly critical due to the distributed nature of modern computing environments. Confidential computing techniques, such as trusted execution environments (TEEs) and homomorphic encryption, provide a promising approach to secure sensitive data while it is being processed across edge and cloud systems. However, challenges persist in achieving efficient and secure computations due to the dynamic and decentralized characteristics of these environments. This research proposes a novel framework that leverages confidential computing technologies to optimize data privacy and security across heterogeneous edge-to-cloud architectures. The framework integrates TEEs with advanced encryption methods to ensure secure processing of sensitive data while maintaining low latency and high throughput. The proposed model is evaluated using several real-world edge-to-cloud datasets and scenarios, focusing on the performance in terms of data confidentiality, computational efficiency, and scalability. Experimental results demonstrate that the proposed framework outperforms existing solutions, achieving enhanced security without compromising system performance. The findings highlight the potential of confidential computing in enabling secure, distributed computations across edge-to-cloud environments, ensuring both privacy and security in emerging decentralized computing paradigms.
Data Privacy; Edge-To-Cloud Architecture; Confidential Computing; Trusted Execution Environments; Homomorphic Encryption; Decentralized Environments; Security; Computational Efficiency
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Ashwini B N and Yashodha H R. Optimizing data privacy and security in heterogeneous edge-to-cloud architectures: Leveraging confidential computing to enable secure distributed computations in decentralized environments. World Journal of Advanced Research and Reviews, 2020, 6(2), 275-280. Article DOI: https://doi.org/10.30574/wjarr.2020.6.2.0125