Towards comprehensive provable data possession in cloud computing

Li, Chaoling; Chen, Yue; Peng-xu, Tan; Yang, Gang

doi:10.1007/s11859-013-0926-8

Cited by 8 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The generalization experiments were conducted on the public datasets, namely the HRC_WHU and SPARCS datasets, to prove the generalizability and validity of our model. The lab from Wuhan University offered the HRC WHU dataset [55]. The dataset contains 150 high-resolution images from around the world, with resolutions ranging from 0.5 to 15 m. The images come from Google Earth, and only clouds were labelled.…”

Section: Experimental Datasetsmentioning

confidence: 99%

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Xia

Zhang

et al. 2023

IJGI

View full text Add to dashboard Cite

Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their shadows in remote sensing imagery, this paper provides a multi-supervised feature fusion attention network. We design a multi-scale feature fusion block (FFB) for the problems caused by the complex distribution and irregular boundaries of clouds and shadows. The block consists of a fusion convolution block (FCB), a channel attention block (CAB), and a spatial attention block (SPA). By multi-scale convolution, FCB reduces excessive semantic differences between shallow and deep feature maps. CAB focuses on global and local features through multi-scale channel attention. Meanwhile, it fuses deep and shallow feature maps with non-linear weighting to optimize fusion performance. SPA focuses on task-relevant areas through spatial attention. With the three blocks above, FCB alleviates the difficulties of fusing multi-scale features. Additionally, it makes the network resistant to background interference while optimizing boundary detection. Our proposed model designs a class feature attention block (CFAB) to increase the robustness of cloud detection. The network achieves good performance on the self-made cloud and shadow dataset. This dataset is taken from Google Earth and contains remote sensing imagery from several satellites. The proposed model achieved a mean intersection over union (MIoU) of 94.10% on our dataset, which is 0.44% higher than the other models. Moreover, it shows high generalization capability due to its superior prediction results on HRC_WHU and SPARCS datasets.

show abstract

Section: Experimental Datasetsmentioning

confidence: 99%

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Xia

Zhang

et al. 2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Several studies [68][69][70][71] introduced privacy protection protocols to determine the privacy leakage issues. In [69], the researchers designed a zero-knowledge proof-based privacypreserving scheme with publicly provable data.…”

Section: State-of-the-art Data Integrity Auditng Techniquesmentioning

confidence: 99%

Security and Privacy of Cloud Data Auditing Protocols: A Review, State-of-the-art, Open Issues, and Future Research Directions

Farooq,

Idrus,

Ahmad

et al. 2023

IJACSA

View full text Add to dashboard Cite

Cloud service providers offer a trustworthy and resistant-based storage environment for on-demand cloud services to outsource clients' data. Several researchers and business entities currently adopt cloud services to store their data in remote cloud storage servers for cost-saving purposes. Cloud storage offers numerous advantages to users like scalability, low capital expenses, and data available from any place, anytime, regardless of location and device. However, as the users lose physical access and control over data, the storage service raises security and privacy issues, such as confidentiality, integrity, and availability of outsourced data. Data integrity is a primary concern for cloud users to confirm whether data integrity is intact or not. This paper presents a comprehensive review of cloud data auditing schemes and a comparative analysis of the desirable features. Furthermore, it provides advantages and disadvantages of the state-of-the-art techniques and a performance comparison regarding the communicational and computational costs of involved entities. It also highlights desirable features of different techniques, open issues, and future research trends of cloud data auditing protocols.

show abstract

“…The TPA performs the auditing without fetching the data copy, resultant, privacy is maintained [12].Li and his colleagues has put forth a feasible method of verifiable data ownership which utilizes a sequence number (SN)-chunk number (BN) vector for assisting data block change. The method is made successful using decreased estimation, stockpiling/data storage and communication overhead [13] [17].Ni and his colleagues presents a security-assurance checking rule related to the dispersed stockpiling as analysed. It's possible for the dynamic attacker to modify the proof of checking processso as to mislead the verifier and acquire the information, resultant, the cloud records that are inaccessible are not corrupted, though the reports are deteriorated [14] [18].…”

Section: Related Workmentioning

confidence: 99%

Public Auditing In Cloud Environment For Secure Data Sharing

Jesudoss¹,

Lakshmanan²,

Christy³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

View full text Add to dashboard Cite

The Cloud storage resembles a service offered by the cloud computing which involves maintaining, managing and backing up data remotely and there by making it accessible for multiple users across the network. Since the user's data can be altered by external users, storing data with in the cloud tends to be a serious thought for the users. To resolve the above issue an approach of data auditing is presented that performs data integrity using the CA (Cloud Auditor) component. The research aims to build an auditing scheme that being effective, secure and capable of public auditing and maintaining integrity and confidentiality of data. A novel successive single cloud data auditing has been proposed where in user provides uninterrupted declarations to the user and evaluates the information which is being strongly founded by adopting the new data audit validating protocol.

show abstract

Towards comprehensive provable data possession in cloud computing

Cited by 8 publications

References 3 publications

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Security and Privacy of Cloud Data Auditing Protocols: A Review, State-of-the-art, Open Issues, and Future Research Directions

Public Auditing In Cloud Environment For Secure Data Sharing

Contact Info

Product

Resources

About