Privacy is of crucial importance in the era of global connectivity in which everything is inter-connected anytime and everywhere, with almost 2 billion world-wide users with connection to the Internet. Indeed, most of these users are concerned about their pri- vacy. These concerns also apply for the new emerging research fields in computer science such as Multi-agent Systems. A Multi-agent System consists of a number of agents (which can be intelligent and/or autonomous) that interact with one-another. An agent usually encapsulates personal information describing its principal (names, preferences, tastes, credit card numbers, etc.). Moreover, agents carry out interac- tions on behalf of their principals. As a result, agents usually exchange personal information about their principals. This may have a direct impact on their principals’ privacy.

In this thesis, we focus on avoiding undesired information collection and infor- mation processing in Multi-agent Systems. In order to avoid undesired information collection we propose a decision-making model for agents to decide whether or not to disclose personal information to other agents is acceptable or not. We also con- tribute a secure Agent Platform that allow agents to communicate with each other in a confidential fashion, i.e., external third parties cannot collect the information that two agents exchange. 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, i.e., the degree by which personal informa- tion can be directly attributed to a particular principal. Finally, we describe how we implemented this model into an existing agent platform.