Privacy Protection Technology Based on Big Data Technology
DOI:
https://doi.org/10.62051/ijcsit.v3n2.02Keywords:
Big data, Privacy protection, Computer technology, AlgorithmsAbstract
In the era of big data, with the development of the Internet industry, privacy protection has become a non-negligible part of network security. At the same time, big data technology has also been integrated into various industries, which is of great significance to the development of all walks of life. The inability of security technology to meet the rapidly evolving needs of this field has become an urgent problem in many fields. Network technology, cloud computing technology, and artificial intelligence have all taken the lead in the development of the front-end of the industry, making them play a role on the Internet platform in the era of big data. At this time, the lack of privacy and security protection has become a huge loophole that endangers the security of personal information and personal property. In order to maintain normal development, it is necessary to invest technologies such as big data in the research of privacy protection. Big data technology has significant advantages in information analysis insight, information integration, and data mining capabilities. The development and utilization of new privacy-preserving technologies have been developed and utilized to address the privacy concerns of the new era. This article will provide several mainstream and relatively complete privacy protection technologies based on the main problems faced by privacy protection. Through the different integration of several mainstream technologies, a variety of privacy protection technologies that can deal with problems in different directions have been formed, acting in different fields, so that users' privacy and security can be more perfectly protected.
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References
Feng, H., Yi, H. (2023) A review of privacy protection research on recommender systems. Computer Science and Exploration, 17
Li, W., Wu, H. (2023) A review of location privacy protection based on semantics. Computer applications, 43
Lei, C., Zhang, L. (2023) Personalized semantically sensitive trajectory data publishing algorithms. Small and micro computer systems, 9
Yang, S. (2023) A personal information privacy protection method based on explicit and implicit feedback. Information Science, 21(11)
Xiang, Y., Yang, L. (2023) Research on power line loss data sharing based on differential privacy protection. Computer applications and software, 40
Sun, G., Wan, M. (2023) Analysis of Blockchain Transaction Privacy Protection. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition)
Sun, D., Li, N. (2023) A differential privacy protection algorithm that integrates explicit and implicit feedback collaborative filtering. Computer Applied Research., 38(08)
DWORK C, MCSHERRY F, NISSIM K, R.A. (2006) Calibrating noise to sensitivity in private data analysis. The 21st International Symposium on Computer and Information Sciences, Istanbul, 265-284
Li, Z. (2023) Design and implementation of medical information release system based on global K-anonymity. Thesis of China University of Mining and Technology
Yao, J. (2023) Research and implementation of user privacy protection query optimization algorithm for spatiotemporal data based on Spark. Thesis of Northeastern University
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