Privacy Protection and User Traceability in Strong Key-Exposure Resilient Auditing for Cloud Storage

Author(s):  
R. Ahila ◽  
S. Sivakumari
2014 ◽  
Vol 36 (3) ◽  
pp. 454-464 ◽  
Author(s):  
Lihong Guo ◽  
Jian Wang ◽  
He Du

2015 ◽  
Vol 10 (6) ◽  
pp. 1167-1179 ◽  
Author(s):  
Jia Yu ◽  
Kui Ren ◽  
Cong Wang ◽  
Vijay Varadharajan
Keyword(s):  

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
S. Mary Virgil Nithya ◽  
V. Rhymend Uthariaraj

Secured storage system is a critical component in cloud computing. Cloud clients use cloud auditing schemes to verify the integrity of data stored in the cloud. But with the exposure of the auditing secret key to the Cloud Service Provider, cloud auditing becomes unsuccessful, however strong the auditing schemes may be. Therefore, it is essential to prevent the exposure of auditing secret keys, and even if it happens, it is necessary to minimize the damage caused. The existing cloud auditing schemes that are strongly resilient to key exposure are based on Public Key Infrastructure and so have challenges of certificate management/verification. These schemes also incur high computation time during integrity verification of the data blocks. The Identity-based schemes eliminate the usage of certificates but limit the damage due to key exposure, only in time periods earlier to the time period of the exposed key. Some of the key exposure resilient schemes do not provide support for batch auditing. In this paper, an Identity-based Provable Data Possession scheme is proposed. It protects the security of Identity-based cloud storage auditing in time periods both earlier and later to the time period of the exposed key. It also provides support for batch auditing. Analysis shows that the proposed scheme is resistant to the replace attack of the Cloud Service Provider, preserves the data privacy against the Third Party Auditor, and can efficiently verify the correctness of data.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 131723-131740 ◽  
Author(s):  
Pan Yang ◽  
Naixue Xiong ◽  
Jingli Ren

2019 ◽  
pp. 470-482
Author(s):  
Xinwei Sun ◽  
Zhang Wei

With the rapid development of cloud storage technology, the cloud storage platform has gradually been used to store data. However, the privacy protection strategy provided by public cloud storage platform is hard to be trust by users. Moreover, they are unable to customize their own storage strategy according to their demands. This study proposed a consistency-availability-partition tolerance (CAP) theory -based data privacy protection strategy, which firstly employed CAP theory to provide privacy data protection for users and then offer users with choice to select corresponding privacy strategy to store data. Moreover, a total of three privacy protection strategies were put forward, focusing on the balance between data consistency and response time, data consistency and data availability, as well as response time and availability respectively.


2011 ◽  
Vol 55-57 ◽  
pp. 504-507
Author(s):  
Jian Hua Zhang ◽  
Nan Zhang ◽  
Chun Chang Fu

The storage security technology in cloud storage applications was analyzed, and in order to satisfied the demand for privacy protection, the key technology of data encryption and authentication are described and the methods of privacy protection in data mining under the cloud were discussed. At the same time, a hierarchical mechanism of authentication was proposed. These methods and mechanisms could solve the problem of privacy protection in a certain degree, and ensure the security of cloud storage.


2016 ◽  
Vol 145 (15) ◽  
pp. 11-14 ◽  
Author(s):  
V. Goutham ◽  
B. Mounika ◽  
P. Shiva
Keyword(s):  

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jianguo Zheng ◽  
Jinming Chen

To solve the current privacy leakage problems of cloud storage services, research on users’ payment intention for cloud storage services with privacy protection is extremely important for improving the sustainable development of cloud storage services. An evolutionary game model between cloud storage users and providers that considers privacy is constructed. Then, the model’s evolutionary stability strategies via solving the replication dynamic equations are analyzed. Finally, simulation experiments are carried out for verifying and demonstrating the influence of model parameters. The results show that the evolutionary stable strategies are mainly affected by the privacy protection profit growth coefficient of both parties, input costs, free-riding gains, and other factors. If the profit growth coefficient is very small, users will not choose to pay and providers will not choose to actively protect user information. As the profit growth coefficient increases, both parties will promote the development of privacy protection with a higher probability. The results are beneficial for cloud storage providers to increase the number of paid users and thus to achieve the sustainable development of cloud storage service.


2019 ◽  
Vol 472 ◽  
pp. 223-234 ◽  
Author(s):  
Xiaojun Zhang ◽  
Huaxiong Wang ◽  
Chunxiang Xu

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