A survey on DE – Duplication schemes in cloud servers for secured data analysis in various applications

被引：0

作者：

Pragash K. ^{[1
]}

Jayabharathy J. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, Puducherry Technological University, Puducherry

来源：

Measurement: Sensors | 2022年 / 24卷

关键词：

Deduplication; Performance analysis; Prediction and classification; Security;

D O I：

10.1016/j.measen.2022.100463

中图分类号：

学科分类号：

摘要：

Data deduplication in a system perspective is termed to be a single or multiple copy of an original data which could increase the computational complexity while accessing such a data. In clinical terms the deduplication is considerably the multiple copy of a same sample. The data deduplication is also vulnerable towards security issues which in turn could affect the performance of a cloud server. This type of several copies had to be handled not only to analyze the data but also to identify the underlying patterns which could support in prediction and classification of the data. There are several deduplication handling schemes in multiple domains had been proposed to handle the repetition of the data from which the most effective and recently proposed schemes had been considered for survey. The deduplication schemes are compared in terms of their performance and their pros and cons are discussed in this article which could pave a path for researchers to propose and perform a better analysis with the deduplication issues. © 2022 The Authors

引用

共 26 条

[1] Meyer D.T., Bolosky W.J., A study of practical deduplication, ACM Trans. Storage, 7, 4, (2012)
[2] Heo H., Ahn C., Kim D.H., Parity data de-duplication in all flash array-based OpenStack cloud block storage, IEICE Trans. Info Syst., 5, pp. 1384-1387, (2016)
[3] Nayak S.K., Tripathy S., SEDS: secure and efficient server-aided data deduplication scheme for cloud storage. inernational, J. Inf. Secur., 19, pp. 229-240, (2020)
[4] Elhoseny M., Shankar K., Lakshmanaprabu S.K., Et al., Hybrid optimization with cryptography encryption for medical image security in Internet of Things, Neural Comput. Appl., 32, pp. 10979-10993, (2020)
[5] Anderson P., Zhang L., Fast and secure laptop backups with encrypted de-duplication, Proceedings of the 24th International Conference on Large Installation System Admin, pp. 29-40, (2010)
[6] Balajee A., Venkatesan R., A survey on classification methodologies utilized for classifying the knee joint disorder levels using vibroarthrographic signals, Mater. Today Proc., (2021)
[7] Alphonse B., Rajagopal V., Sengan S., Et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J. Ambient Intell. Hum. Comput., (2021)
[8] Tyagi V., Karanam H., Faruquie T., Subramaniam L., Ratha N., Fusing biographical and biometric classifiers for improved person identification, Proceedings of the International Conference on Pattern Recognition, (2012)
[9] Bellare M., Keelveedhi S., Ristenpart T., Dupless: server-aided encryption for deduplicated storage, Proceedings of the 22nd USENIX Conference on Security, pp. 179-194, (2013)
[10] Sewak Sudhish P., Jain A.K., Cao K., Adaptive fusion of biometric and biographic information for identity de-duplication, Pattern Recogn. Lett., 84, pp. 199-207, (2016)

← 1 2 3 →