5 Opinions for Big Data Transfer Security Technology
Big data transfer is becoming a new driving force for economic and social development, and is increasingly affecting economic operations, lifestyles and national governance capabilities. The security of big data movement has been improved to the level of national security. Based on the challenges and problems facing big data transfer security and the development of big data security technology, we put forward the following 5 opinions for the development of big data security technology.
5 Opinions for Big Data Transfer Security Technology
1. Build an integrated big data security defense system from the perspective of an overall security
Security is a prerequisite for development. It is necessary to comprehensively improve the security of big data security technology, and then establish a comprehensive three-dimensional defense system that runs through the cloud management of big data applications to meet the needs of both countries. Big data strategy and it's market application.
First, it is necessary to establish a security protection system covering the entire data life cycle, from collection to transfer, storage, processing, sharing, and final destruction. It is necessary to fully utilize data source verification, encryption of large-scale data transfer, encrypted storage in non-relational databases, privacy protection, data transaction security, prevention of data leakage, traceability, data destruction, and other technologies.
The second is to enhance the security defense capabilities of the big data platform itself. It should introduce authentication for users and components, fine-grained access control, security audits for data operations, data desensitization, and other such privacy protection mechanisms. It is necessary to prevent unauthorized access to the system and data leakage while increasing attention to the inherent security risks involved in the configuration and operation of big data platform components. It is necessary to enhance the ability to respond to emergency security incidents that occur on the platform.
Finally, it uses big data analysis, artificial intelligence, and other technologies to automatically identify threats, prevent risks and track attacks, and transition from passive defense to active detection. Ultimately, the goal is to enhance the security of big data from the bottom up and enhance the ability to defend against unknown threats.
2. Starting from attack defense, strengthen the security protection of big data platforms
Platform security is the cornerstone of big data system security. From an earlier analysis, we can see that the nature of cyberattacks against big data platforms is changing. Enterprises are facing increasingly serious security threats and challenges. Traditional defensive surveillance methods will find it difficult to keep up with this change in the threat landscape. In the future, research on the security technology of big data transfer platforms should not only solve operational security issues but also design innovative big data platform security protection systems to adapt to the changing nature of cyber attacks. In terms of security protection technology, both open source and commercial big data platforms are in a stage of rapid development. However, the cross-platform security mechanism still has shortcomings. At the same time, the development of new technologies and new applications will reveal platform security risks that are not yet known. These unknown risks require all parties in the industry to start from the offensive and defensive aspects, invest more in the security of the big data platform, and pay close attention to the development trend of big data network attacks and defense mechanisms. It is necessary to establish a defense system suitable for and build a more secure and reliable big data platform.
3. Use key links and technologies as breakthrough points to improve the data security technology system
In the big data environment, data plays a value-added role, its application environment is becoming more and more complex, and all aspects of the data life cycle are facing new security requirements. Data collection and traceability have become prominent security risks, and cross-organizational data cooperation is extensive, leading to confidentiality protection requirements that trigger multi-source aggregate computing. At present, technologies such as sensitive data identification, data leakage protection, and database security protection are relatively mature, while confidentiality protection in multi-source computing, unstructured database security protection, data security early warning, emergency response, and traceability of data leakage incidents, still relatively weak. Actively promote the development of industry-university-research integration, and accelerate the research and application of key technologies such as ciphertext calculations to improve computing efficiency.
Enterprises should strengthen support for data collection, calculation, traceability, and other key links; Strengthen data security monitoring, early warning, control, and emergency response capabilities; Take data security key links and key technology research as a breakthrough; Improve the big data security technology system; To promote the healthy development of the entire big data industry.
4. Strengthen the investment in the industrialization of privacy protection core technologies, while considering the two important priorities of data use and privacy protection
In the big data application environment, data usage and privacy protection will naturally conflict. Homomorphic encryption, secure multi-party computing, and anonymization technologies can strike a balance between the two and are ideal technologies to solve the privacy challenges in the application of big data. The advancement of core privacy protection technologies will inevitably greatly promote the development of big data applications. Currently, the core problem of privacy protection technology is efficiency, and its problems include high computing costs, high storage requirements, and lack of evaluation standards.
Some researches, in theory, have not been widely used in engineering practice. It is very difficult to deal with privacy security threats such as multiple data sources or statistics-based attacks. In the big data environment, personal privacy protection has become a topic of much concern, and with the increasing demand for privacy protection in the future, it will drive the development and industrial application of dedicated privacy protection technologies. To improve the level of privacy protection technology in the big data environment, we must encourage enterprises and scientific research institutions to study privacy protection algorithms such as homomorphic encryption and secure multi-party computing, and at the same time promote data desensitization, audit applications, and other technical methods.
5. Pay attention to the research and development of big data security review technology and build a third-party security review system
At present, the state has formulated a series of major decision-making arrangements for big data security. The government promotes the deep integration of big data and the real economy and emphasizes the need to effectively protect national data security. The National Informatization Plan puts forward an implementation plan for the big data security project. It is foreseeable that the government's supervision of big data security will be further strengthened in the future, the legislative process related to data security will be further accelerated, big data security supervision measures and technical means will be further improved, and the disciplinary work of big data security supervision will be further strengthened.
Previous:What Does File Transfer Acceleration Mean?
Next: How to Improve the Security of Big Data Transfer?