Privacy Preserving for Big Data in Mobile Cloud-Computing using Encryption Strategy
Main Article Content
Abstract
Big data applications are currently emerging technology in cloud computing. However working on big data applications brings its own challenge of privacy and security issues; larger data sizes leads to complex privacy issues. The implementation and adoption of these big data applications have redefined service models and enhanced application performances in several aspects. Data processing and transmissions reveal serious issues related to execution time of data encryption. Applications abstain from data encryptions to obtain an adoptive performance level conjunction with privacy concerns. This paper emphasizes on privacy and affirms atypical data encryption approach called Dynamic Data Encryption Strategy (D2ES). The performance of D2ES has been evaluated which confides the privacy enhancement. Our planned methodology is to selectively encrypt data and use privacy classification methods under timing constraints. This employs selective encryption strategy within the required execution time to capitalize the privacy protection scale.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
K. Gai, L. Qiu, M. Chen, H. Zhao, and M. Qiu. SAEAST: security ware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Transactions on Embedded Computing Systems, 16(2):60, 2017.
S. Yu, W. Zhou, S. Guo, and M. Guo. A feasible IP trace back framework through dynamic deterministic packet marking. IEEE Transactions on Computers, 65(5):1418–1427, 2016.
L. Wu, K. Wu, A. Sim, M. Churchill, J. Choi, A. Stathopoulos, C. Chang, and S. Klasky. Towards realtime detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma. IEEE Transactions on Big Data, 2(3), 2016.
Y. Li, W. Dai, Z. Ming, and M. Qiu. Privacy Protection for Preventing Data Over-Collection in Smart City. IEEE Transactions on Computers, 65:1339–1350, 2015.
S. Yu,W. Zhou,W. Jia, S. Guo, Y. Xiang, and F. Tang. Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Transactions on Parallel and Distributed Systems, 23(6):1073– 1080, 2015
Y. Wu, K. Wu, A. Sim, M. Churchill, J. Choi, A. Stathopoulos, C. Chang, and S. Klasky. Towards realtime detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma. IEEE Transactions on