论文标题
推论攻击下的社会学习
Social learning under inferential attacks
论文作者
论文摘要
社会学习文献中的一个共同假设是，代理人以无私的方式交换信息。在这项工作中，我们考虑了一部分代理旨在将网络信念推向错误假设的情况。对手没有意识到真正的假设。但是，它们将通过与其他代理相似的行为“融合”，并会操纵信念更新过程中用于发射推论攻击的可能性功能。我们将表征网络被误导的条件。然后，我们将解释说，这种攻击有可能通过证明存在恶意代理人为此目的采用的策略来取得成功。我们研究了代理商对网络模型的最小信息或没有信息的两种情况。
A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model.
