Encrypted transmission method of sensitive data in energy big data center based on AES algorithm

Big data has great commercial importance to major businesses, but security and privacy challenges are also daunting this storage, processing, and communication. Big data encapsulate organizations' most important and sensitive data with multi-level complex implementation. The challenge for any organization is securing access to the data while allowing end user to extract valuable insights. Unregulated access privileges to the big data leads to loss or theft of valuable and sensitive. Privilege escalation leads to insider threats. Also, the computing architecture of big data is not focusing on session recording; therefore, it is becoming a challenge to identify potential security issues and to take remedial and mitigation mechanisms. Therefore, various big data security issues and their defense mechanisms are discussed in this chapter.

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Research on Data Center Operation and Maintenance Management Based on Big Data

2020 International Conference on Computer Engineering and Application (ICCEA) ◽

10.1109/iccea50009.2020.00033 ◽

2020 ◽

Author(s):

Li Jiadi ◽

Huang Yang ◽

Li Huan ◽

Zeng Xinli ◽

Li WenJing

Keyword(s):

Big Data ◽

Data Center ◽

Maintenance Management ◽

Operation And Maintenance

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System- Wide Sustainability Factors and Strategies for Big Data Center

2018 15th International Conference on Service Systems and Service Management (ICSSSM) ◽

10.1109/icsssm.2018.8465035 ◽

2018 ◽

Author(s):

Qingyu Zhang ◽

Shimiao Yang ◽

Xizhi Zheng ◽

Mei Cao

Keyword(s):

Big Data ◽

Data Center

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Evaluating Encryption Algorithms for Sensitive Data Using Different Storage Devices

Scientific Programming ◽

10.1155/2020/6132312 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Bahman A. Sassani (Sarrafpour) ◽

Mohammed Alkorbi ◽

Noreen Jamil ◽

M. Asif Naeem ◽

Farhaan Mirza

Keyword(s):

Flash Memory ◽

Full Range ◽

Data Encryption ◽

Storage Device ◽

Sensitive Data ◽

Hard Drives ◽

Storage Devices ◽

Aes Algorithm ◽

Encryption Algorithms ◽

Storage Encryption

Sensitive data need to be protected from being stolen and read by unauthorized persons regardless of whether the data are stored in hard drives, flash memory, laptops, desktops, and other storage devices. In an enterprise environment where sensitive data is stored on storage devices, such as financial or military data, encryption is used in the storage device to ensure data confidentiality. Nowadays, the SSD-based NAND storage devices are favored over HDD and SSHD to store data because they offer increased performance and reduced access latency to the client. In this paper, the performance of different symmetric encryption algorithms is evaluated on HDD, SSHD, and SSD-based NAND MLC flash memory using two different storage encryption software. Based on the experiments we carried out, Advanced Encryption Standard (AES) algorithm on HDD outperforms Serpent and Twofish algorithms in terms of random read speed and write speed (both sequentially and randomly), whereas Twofish algorithm is slightly faster than AES in sequential reading on SSHD and SSD-based NAND MLC flash memory. By conducting full range of evaluative tests across HDD, SSHD, and SSD, our experimental results can give better idea for the storage consumers to determine which kind of storage device and encryption algorithm is suitable for their purposes. This will give them an opportunity to continuously achieve the best performance of the storage device and secure their sensitive data.

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Database Resources of the National Genomics Data Center in 2020

Nucleic Acids Research ◽

10.1093/nar/gkz913 ◽

2019 ◽

Cited By ~ 24

Author(s):

◽

Zhang Zhang ◽

Wenming Zhao ◽

Jingfa Xiao ◽

Yiming Bao ◽

...

Keyword(s):

Big Data ◽

Data Integration ◽

Data Center ◽

Lower Cost ◽

Value Added ◽

Omics Data ◽

The Past ◽

Sequencing Technologies ◽

Research Activities ◽

Genome Databases

Abstract The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

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