The top 10 data security hotspots of 2018 are here, how much do you know?
The year 2018 is like a white horse passing by, with the "full law of network security", "the basic requirements of network security level protection" and "national cyberspace security strategy" and other party central working documents and major strategic deployment, data security issues are more and more attention, data security protection system is more and more sound, not in the just concluded "2018 China Information and Communication Conference", announced the majority of netizens selected the top ten hot data security, it can be said that each of them are at the forefront of technology. Let's take a look at what the top 10 hot spots are, shall we?
I. Platforming and ecologizing data protection
With big data gradually becoming an important strategic resource for the country, governments and enterprises have reached a consensus that "the original single-point protection means for data assets can no longer meet the demand for data protection." At this stage, we should jointly promote the construction of an integrated data protection platform by the government and enterprises, promote the standardization and systematization of data asset protection, and on this basis, further coordinate multiple resources to build a multi-user networked and collaborative data protection ecology.
II. Full life-cycle management of data
The full data life cycle consists of six phases: data capture, transmission, storage, processing, exchange and destruction. Around the various aspects of the whole life cycle of data, it is necessary to improve the technical capabilities of enterprise data security protection, and to manage the whole life cycle of data from collection to final destruction.
III. Big data mining
Big data has become a new type of production factor for society and a basic strategic resource for the country, and its wide application will generate huge socio-economic value, help reshape the country's competitive advantage and enhance the level of governance modernization. Countries around the world have taken the digitization of their economies as an important driving force for achieving innovative development, and are actively promoting the research and development of cutting-edge technologies for big data mining and the social promotion of service applications.
IV. Data value identification
The identification of sensitive and important data is the core aspect of data security protection, and through automated means or manual methods, certain value measurement and classification methods are adopted to conduct in-depth analysis of data assets, identify and analyze core data assets, important data assets and general data assets, and achieve focused protection of key assets, which can avoid the waste of limited security resources.
V. Data security assessment
Data security assessment is an important branch in the field of information security risk assessment, which assesses possible data security risks and judges the effectiveness of data security measures in accordance with the whole data life cycle process. Data security assessment plays an important and fundamental role in safeguarding data security, optimizing the development environment, and guiding industry innovation.
VI. Data classification
The core concept of data classification is to classify different security levels and formulate corresponding security measures according to the value of data assets and the level of impact caused by data security events such as leakage, theft and destruction. The concept of graded protection can avoid the drawbacks of the traditional one-size-fits-all management of data security, formulate targeted data security measures, maximize the value of data assets while fully protecting the security of data assets, and effectively guarantee the healthy development of the digital security industry.
VII. Data desensitization
Data desensitization is an important aspect of data security that has been widely recognized and valued by the industry. Data desensitization refers to the deformation of sensitive privacy data by desensitization rules, which ensures to a certain extent that the real information of users cannot be inverted based on the desensitized data, and data desensitization can enable reliable protection of personal privacy while mining the value of big data and maximizing the potential of data analysis and mining. Given the role of data desensitization on data security and data value, research on data desensitization techniques needs to be further strengthened to achieve a balance between ensuring data security and maximizing data value.
VIII. Privacy compliance
At this stage, countries around the world attach great importance to work related to personal privacy protection and actively promote the protection and management of personal privacy. In 2018, the United States and Europe have issued the Act on Clarifying the Lawful Use of Offshore Data, the General Data Protection Regulation and other relevant policies and regulations, focusing on the collection, processing, sharing, deletion and other whole life cycle aspects of personal information to strengthen compliance management and protect the security of personal information and the legitimate rights and interests of personal information subjects. China's Cybersecurity Law of the People's Republic of China, which officially came into effect on June 1, 2017, also emphasizes the legal responsibilities and non-compliance measures that network operators in China should take for the personal information they collect, while there is news that a related law for personal privacy will also be released in the first half of 2019.
IX. Data validation
Data rights are one of the core issues that must be addressed in the development of big data applications and data industries. Data rights are defined for data from different sources, and the ownership of their property rights is clarified in legal form to promote efficient data utilization and deep mining, accelerate data sharing and circulation, and reduce transaction costs, thus activating the value of huge data assets and innovative applications, and enabling the rapid development of the digital security industry.
X. Data traceability
Data traceability can be achieved by using big data technologies such as data tagging, data analytics, and reverse query to retrace the path of data flow within the full lifecycle of data. Data traceability is currently applied in data auditing, data authenticity assessment, and data access control, which can effectively assist data security regulatory activities and enhance the ability of governments and enterprises to manage data security.