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A randomized block policy gradient algorithm with differential privacy in Content Centric Networks

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Listed:
  • Lin Wang
  • Xingang Xu
  • Xuhui Zhao
  • Baozhu Li
  • Ruijuan Zheng
  • Qingtao Wu

Abstract

Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the ε -privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment.

Suggested Citation

  • Lin Wang & Xingang Xu & Xuhui Zhao & Baozhu Li & Ruijuan Zheng & Qingtao Wu, 2021. "A randomized block policy gradient algorithm with differential privacy in Content Centric Networks," International Journal of Distributed Sensor Networks, , vol. 17(12), pages 15501477211, December.
  • Handle: RePEc:sae:intdis:v:17:y:2021:i:12:p:15501477211059934
    DOI: 10.1177/15501477211059934
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    1. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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