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Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network
- Source :
- Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-15 (2023)
- Publication Year :
- 2023
- Publisher :
- SpringerOpen, 2023.
-
Abstract
- Abstract Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.
Details
- Language :
- English
- ISSN :
- 2192113X
- Volume :
- 12
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Cloud Computing: Advances, Systems and Applications
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5ef07f48e9264b63b9ffa8a640de6e51
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s13677-023-00537-0