| Paper ID | SPTM-19.1 |
| Paper Title |
SOCIAL LEARNING UNDER INFERENTIAL ATTACKS |
| Authors |
Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
| Session | SPTM-19: Inference over Graphs |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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 net-work 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. |
