Multi-layer access control for cloud data using improved DBSCAN, AES, homomorphic encryption, and RMAAC for text, image, and video
DOI:
https://doi.org/10.22399/ijcesen.837Keywords:
DBSCAN, CLOUD, RMAAC, AES, Security, homomorphic encryptionAbstract
With the growing demand for cloud storage solutions that can handle diverse data types such as text, images, and videos, ensuring robust access control and security becomes important. This paper proposes a novel multi-layer access control framework for cloud environments, incorporating advanced Clustering Filtering Techniques with an Improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to enhance search efficiency across multiple data layers. This clustering approach enables quick and accurate retrieval of text, image, and video content by efficiently organizing data based on similarity. To ensure data privacy and security, employs a hybrid encryption approach, combining Advanced Encryption Standard (AES) for data at rest and Homomorphic Encryption (HE) for data in use, allowing secure data manipulation without compromising confidentiality. The access control mechanism is further strengthened by introducing a Role-based Multi-Attribute Access Control (RMAAC) model, which grants permissions based on a user’s role, attributes, and the sensitivity level of the data being accessed. This fine-grained control restricts unauthorized access while supporting flexible policies for different data types. Simulation results demonstrate that the proposed framework significantly improves data retrieval speed, security, and clustering performance, making it an effective solution for cloud storage systems handling diverse media formats.
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Dataset taken for image and video from: “https://www.kaggle.com/competitions/mu-cifar10”, dated 1/11/2024.
Dataset taken for text from: “https://scikit-learn.org/dev/modules/generated/sklearn.datasets.fetch_20newsgroups.html”, dated 1/11/2024.
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