A Survey of Privacy Protection and Network Security in User On-Demand Anonymous Communication
Autor: | Xiaolong Yang, Yiming Chen, Min Zhang, Jingtang Luo, Yun He |
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Rok vydání: | 2020 |
Předmět: |
General Computer Science
Network security Computer science Data_MISCELLANEOUS Computer security computer.software_genre traffic classification attacks network security Privacy protection General Materials Science Private information retrieval business.industry General Engineering Eavesdropping machine learning Traffic classification Identity (object-oriented programming) ComputingMilieux_COMPUTERSANDSOCIETY user on-demand anonymous communication The Internet lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 computer Anonymity |
Zdroj: | IEEE Access, Vol 8, Pp 54856-54871 (2020) |
ISSN: | 2169-3536 |
Popis: | In an untrusted Internet environment, there is a large amount of user privacy information being vulnerable to various attacks, and it is an important security concern for users on how to protect their privacy, and to further provide the anonymity guarantee. Recently, most privacy-sensitive users prefer anonymous communication to protect their private information from eavesdropping by attackers. Each user has different privacy protection demands for anonymous communication. However, anonymous communication methods cannot meet the various anonymity needs of users. Hence, the user on-demand anonymous communication is proposed, which can dynamically adjust the anonymity level according to a user's anonymity needs. However, the defense capabilities against attacks are insufficient in the existing user on-demand anonymous communication. Some malicious users in the network employ anonymous communication to hide their identities and attack the Internet. Moreover, the existing user on-demand anonymous communication is weak against traffic classification attacks based on machine learning. Therefore, this paper investigates its progresses and defects in privacy protection and network security. User on-demand anonymous communication is mainly composed of the user side and the transmission side. We separately investigate privacy protection and network security of user on-demand anonymous communication from the user side and the transmission side. By surveying various identity anonymity of users on the user side and the hierarchical anonymous transmission on the transmission side, we find two kinds of serious attacks in user on-demand anonymous communication, i.e., abuse of anonymous communication and traffic classification attacks based on machine learning. To solve the above security issues, we suggest the balancing mechanism of anonymous communication and behavior tracking which is used to prevent anonymous abuse, and the secure defense mechanism which is used to resist traffic classification attacks. To inspire follow-up research, we identify some open problems and emphasize future trends concerning user on-demand anonymous communication. |
Databáze: | OpenAIRE |
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