[2022] How to Improve the Security of Big Data Transfer?

Facing the challenges and threats related to the security of big data transfer, the industry has conducted targeted practices and investigations on security protection technologies. This article focuses on three aspects of the development of big data security technology: platform security, data security, and privacy protection.

How to securely improve big data transfer?

Technologies related to platform security, data security, and privacy protection are improving, allowing us to solve big data security issues and challenges. However, to respond to new methods of cyber attacks, protect new data applications, and meet increased privacy protection requirements, higher standards and functions will be required.

Improve platform security

In terms of platform technology, centralized security configuration management and security mechanism deployment can meet the security requirements of the current platform. However, vulnerability scanning and attack monitoring technologies for big data platforms are relatively weak.

In terms of technologies for defending platforms from network attacks, current big data platforms still use traditional network security measures to defend against attacks. This is not enough for the big data environment. In the big data environment, the extensible defense boundary is vulnerable to attack methods that cover up the intrusion. Besides, the industry pays little attention to potential attack methods that may come from the big data platform itself. Once new vulnerabilities appear, the scope of the attack will be huge.

Improve data security

In terms of data security, data security monitoring and anti-sabotage technologies are relatively mature, but data sharing security, unstructured database security protection, and data violation traceability technologies need to be improved. Currently, there are technical solutions for data leakage: technology can automatically identify sensitive data to prevent leakage; The introduction of artificial intelligence and machine learning makes the prevention of violations move toward intelligence; The development of database protection technology also provides a powerful way to prevent data leakage guarantee. The ciphertext calculation technology and the data leakage tracking technology have not yet been developed to the extent, which they can meet the needs of practical applications, and it is still difficult to solve the confidentiality assurance problem of data processing and the problems related to tracking data flow. Specifically, the ciphertext calculation technology is still in the theoretical stage, and the calculation efficiency does not meet the requirements of practical applications.

Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications. Data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications.

Improve privacy security

In terms of privacy protection, technological development clearly cannot meet the urgent need for privacy protection. The protection of personal information requires the establishment of a guarantee system based on legal, technical, and economic methods. Currently, the widespread use of data desensitization technology poses challenges to multi-source data aggregation and may lead to failure.

So far, there are few practical application case studies for emerging technologies such as anonymization algorithms, and there are other common problems with this technology, such as low computational efficiency and high overhead. In terms of computing, continuous improvement is needed to meet the requirements of protecting privacy in a big data environment. As mentioned earlier, the conflict between big data applications and personal information protection is not just a technical issue. Without technical barriers, privacy protection still requires legislation, strong law enforcement, and regulations to collect personal information for big data applications. Establish a personal information protection system that includes government supervision, corporate responsibility, social supervision, and self-discipline of netizens.