Visible to the public Biblio

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2023-03-31
Du, Juan.  2021.  Research on Enterprise Information Security and Privacy Protection in Big Data Environment. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). :324–327.
With the development of information technology, extracting important data that people need from the vast information has become the key to a successful era. Therefore, big data technology is increasingly recognized by the public. While creating a lot of commercial value for enterprises, it also brings huge challenges to information security and privacy. In the big data environment, data has become an important medium for corporate decision-making, and information security and privacy protection have become the “army battleground” in corporate competition. Therefore, information security and privacy protection are getting more and more attention from enterprises, which also determines whether enterprises can occupy a place in the fiercely competitive market. This article analyzes the information security and privacy protection issues of enterprises in the big data environment from three aspects. Starting from the importance and significance of big data protection, it analyzes the security and privacy issues of big data in enterprise applications, and finally conducts information security and privacy protection for enterprises. Privacy protection puts forward relevant suggestions.
2020-08-24
Liu, Hongling.  2019.  Research on Feasibility Path of Technology Supervision and Technology Protection in Big Data Environment. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :293–296.
Big data will bring revolutionary changes from life to thinking for society as a whole. At the same time, the massive data and potential value of big data are subject to many security risks. Aiming at the above problems, a data privacy protection model for big data platform is proposed. First, the data privacy protection model of big data for data owners is introduced in detail, including protocol design, logic design, complexity analysis and security analysis. Then, the query privacy protection model of big data for ordinary users is introduced in detail, including query protocol design and query mode design. Complexity analysis and safety analysis are performed. Finally, a stand-alone simulation experiment is built for the proposed privacy protection model. Experimental data is obtained and analyzed. The feasibility of the privacy protection model is verified.