Enhancing privacy in Multi-agent Systems

Abstract

In this thesis, we focus on avoiding undesired information collection and information processing in Multi-agent Systems. In order to avoid undesired information collection we propose a decision-making model for agents to decide whether disclosing personal information to other agents is acceptable or not. We also contribute a secure Agent Platform that allows agents to communicate with each other in a confidential fashion. In order to avoid undesired information processing, we propose an identity management model for agents in a Multi-agent System. This model avoids undesired information processing by allowing agents to hold as many identities as needed for minimizing data identifiability.