Abstract
Human society has entered the era of big data, and massive amounts of data and information are exchanged between application platforms at high speed. In contrast to the development of technology, the leakage of citizens’ personal privacy information in China has shown a spurt in recent years, and how to protect citizens’ privacy has become an urgent issue in today’s society. This paper uses model analysis and comparative analysis to explore personal privacy risks, analyses the internal and external factors affecting personal privacy risks in the era of big data and establishes a “trinity” personal privacy risk assessment model consisting of network service providers, Internet users and Internet regulators. This article also analyzes the model of federated learning framework, Secure multiparty computation, decentralization and Hawk block chain platform and DNN, model for privacy preservation. Suggestions and countermeasures are given in the article to strengthen personal privacy protection. It also provides recommendations and countermeasures for strengthening personal privacy protection.
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References
Wang, S.: Discussion on protection strategy of computer network information security in the era of big data. Comput. Programm. Skills Mainten. 9, 31–32 (2021)
Wang, P., Chen, T., Wang, Z.: Research on privacy preserving data mining. J. Inf. Hiding Privacy Protect. 1(2), 61–68 (2019)
Internet Rule of Law Research , Center: China youth university for political sciences. Personal Privacy Secur. Protect. Rep. 1, 23–24 (2016)
Wang, B.J., Tong, H.: Information security technology system. In: 2nd edn. People Public Security of China, Bei**g China (2018)
Li, T.: Database network security prevention methods under the background of big data. Electron. Technol. Softw. Eng. 6, 3–4 (2021)
**ong, L.: Overview of mobile internet information security regulation in big data era. Netw. Secur. Technol. Appl. 4, 5–6 (2021)
Lu, H.L., Wang, L.M., Yang, J.: A new parameter masks the federal learning privacy protection scheme. Inf. Netw. Secur. 3(8), 26–27 (2021)
Yao, Q.Z.: Protocols for secure computations. IEEE Comput. Soc. 2, 160–164 (1982)
Nithyanantham, S., Singaravel, G.: Hybrid deep learning framework for privacy preservation in geo-distributed data centre. Intell. Autom. Soft Comput. 32(3), 1905–1919 (2022)
Paulraj, D.: A gradient boosted decision tree-based sentiment classification of twitter data. Int. J. Wavelets Multiresol. Inf. Process. 4(18), 205027–1–205027–21 (2020)
Wen, T.Q.: Research on enterprise information security risk and emergency plan. Wuhan Univ. Technol. 12, 13–18 (2016)
Wu, J.Y.: Computer network information security measures under big data. Electron. Technol. Softw. Eng. 9, 2–3 (2021)
Sun, Q.: Analysis on security intensity and development trend of encryption algorithm. Softw. Ind. Eng. 2(3), 29–30 (2016)
Tian, B.: Data security analysis under the background of big data. Netw. Secur. Technol. Appl. 2(7), 7–8 (2021)
Zhang, Y.T.: Legislation and improvement of personal information protection in the era of big data. Legal Rev. 3(15), 148–149 (2021)
Li, P.R., Zhang, F.Q.: Computer Network information security protection strategy and evaluation algorithm. Netw. Secur. Technol. Appl. 3(8), 22–23 (2021)
Wang, P., Wang, Z., Ma, Q.: Research on the association of mobile social network users privacy information based on big data analysis. J. Inf. Hiding Privacy Protect. 1(1), 35–42 (2019)
Jiang, L., Fu, Z.: Privacy-preserving genetic algorithm outsourcing in cloud computing. J. Cyber Secur. 2(1), 49–61 (2020)
Wazirali, R.: A review on privacy preservation of location-based services in internet of things. Intell. Autom. Soft Comput. 2(32), 767–779 (2022)
Acknowledgement
This work was supported by the National Natural Science Foundation of China [Grants No.62071056], Open Research Fund of the Public Security Behavioral Science Laboratory, People’s Public Security University of China [Grants 2020SYS03] and the Fundamental Research Funds for the Central Universities, People’s Public Security University of China (2021JKF215).
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Ye, N., Yuan, D., Meng, Y., Ding, M. (2022). Research on Personal Privacy Risks and Countermeasures in the Era of Big Data. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1588. Springer, Cham. https://doi.org/10.1007/978-3-031-06764-8_18
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DOI: https://doi.org/10.1007/978-3-031-06764-8_18
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