论文标题
如何保护您的隐私？反对抗决策的框架
How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making
论文作者
论文摘要
我们考虑一个反对对抗性的顺序决策问题，代理通过过滤私人信息来计算世界当前状态的私人信念（后验分布）。根据其私人信念，代理人执行了一项行动，这是由对抗者观察到的。我们最近展示了对抗代理如何通过反优化重建决策代理的私人信念。本文的主要贡献是一种通过执行次优的作用来掩盖对手的私人信念的方法。所提出的方法优化了混淆私人信念和限制由于采取次优的行动而产生的成本增加之间的权衡。我们提出了一种概率放松，以获得解决权衡的线性优化问题。在数值示例中，我们表明所提出的方法使代理商能够混淆其私人信念，而不会损害其成本预算。
We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filtering private information. According to its private belief, the agent performs an action, which is observed by an adversarial agent. We have recently shown how the adversarial agent can reconstruct the private belief of the decision-making agent via inverse optimization. The main contribution of this paper is a method to obfuscate the private belief of the agent from the adversary, by performing a suboptimal action. The proposed method optimizes the trade-off between obfuscating the private belief and limiting the increase in cost accrued due to taking a suboptimal action. We propose a probabilistic relaxation to obtain a linear optimization problem for solving the trade-off. In numerical examples, we show that the proposed methods enable the agent to obfuscate its private belief without compromising its cost budget.
