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Evolution of cooperation in malicious social networks with differential privacy mechanisms

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成果类型:
期刊论文
作者:
Zhang, Tao;Ye, Dayong;Zhu, Tianqing*;Liao, Tingting;Zhou, Wanlei
通讯作者:
Zhu, Tianqing
作者机构:
[Ye, Dayong; Zhou, Wanlei; Zhu, Tianqing; Zhang, Tao] Univ Technol Sydney, Sch Comp Sci, Ctr Cyber Secur & Privacy, Sydney, NSW, Australia.
[Liao, Tingting] Wuhan Polytech Univ, Dept Comp Sci, Wuhan, Peoples R China.
通讯机构:
[Zhu, Tianqing] U
Univ Technol Sydney, Sch Comp Sci, Ctr Cyber Secur & Privacy, Sydney, NSW, Australia.
语种:
英文
关键词:
Evolution of cooperation;Reinforcement learning;Differential privacy;Social network
期刊:
Neural Computing and Applications
ISSN:
0941-0643
年:
2023
卷:
35
期:
18
页码:
12979-12994
基金类别:
Australian Research Council, AustraliaAustralian Research Council [DP200100946]
机构署名:
本校为其他机构
院系归属:
数学与计算机学院
摘要:
Cooperation is an essential behavior in multi-agent systems. Existing mechanisms have two common drawbacks. The first drawback is that malicious agents are not taken into account. Due to the diverse roles in the evolution of cooperation, malicious agents can exist in multi-agent systems, and they can easily degrade the level of cooperation by interfering with agent's actions. The second drawback is that most existing mechanisms have a limited ability to fit in different environments, such as different types of social networks. The performance of existing mechanisms heavily depends on some fact...

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