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Privacy Protection in Information and Communication Technology Applications Based on Big Data

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DOI: 10.23977/jeis.2024.090209 | Downloads: 7 | Views: 138


Dongyuan Ge 1, Qianyi Fang 2, Qi Han 1


1 State Grid Heilongjiang Information & Telecommunication Company Ltd, Harbin, Heilongjiang, China
2 Faculty of Science, Northeast Forrstry University, Harbin, Heilongjiang, China

Corresponding Author

Dongyuan Ge


With the advent of the information age, big data has become an important force driving the rapid development of information and communication technology, while also bringing huge challenges to user privacy and security. This article is based on various privacy protection methods such as data anonymity, differential privacy, and ciphertext. Through quantitative and empirical research, the application of big data in information communication was deeply explored. Therefore, this article focused on how to efficiently apply data in network security while ensuring its availability and accuracy. A new data anonymity model was designed to address the issue of data anonymity, which can maximize the protection of user privacy and the availability of user data; the impact of different noise addition strategies on the accuracy of data analysis in differential security systems was studied; on this basis, a study was conducted on the combination of multiple encryption methods to improve their security in storage and transmission. In the latest data security assessment, data encryption led with a high score of 9.5, demonstrating its outstanding performance in protecting data security. This study provided a new approach and method for China's privacy protection in the field of information and communication, which can enhance the country's credibility in the field of public information and communication.


Big Data, Information and Communication Technology Applications, Privacy Protection, Data Encryption


Dongyuan Ge, Qianyi Fang, Qi Han, Privacy Protection in Information and Communication Technology Applications Based on Big Data. Journal of Electronics and Information Science (2024) Vol. 9: 80-87. DOI:


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