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Evolution of cooperation in malicious social networks with differential privacy mechanisms

  • S.I. : Deep Social Computing
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Abstract

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 factors, such as network structures and the initial proportion of cooperators. To solve these two drawbacks, we propose a novel mechanism which adopts differential privacy mechanisms and reinforcement learning. Differential privacy mechanisms can be used to relieve the impact of malicious agents by exploiting the property of randomization. Reinforcement learning enables agents to learn how to make decisions in various social networks. In this way, the proposed mechanism can promote the evolution of cooperation in malicious social networks.

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Acknowledgements

This work is supported by an ARC Discovery Project (DP200100946) from Australian Research Council, Australia.

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Authors and Affiliations

  1. Centre for Cyber Security and Privacy, School of Computer Science, University of Technology Sydney, Sydney, Australia

    Tao Zhang, Dayong Ye, Tianqing Zhu & Wanlei Zhou

  2. Department of Computer Science, Wuhan Polytechnic University, Wuhan, China

    Tingting Liao

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  1. Tao Zhang
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  2. Dayong Ye
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  3. Tianqing Zhu
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  4. Tingting Liao
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  5. Wanlei Zhou
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Correspondence to Tianqing Zhu.

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Zhang, T., Ye, D., Zhu, T. et al. Evolution of cooperation in malicious social networks with differential privacy mechanisms. Neural Comput & Applic 35, 12979–12994 (2023). https://doi.org/10.1007/s00521-020-05243-5

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