Reputation systems provide reputation values of rated parties to users. These reputation values, typically aggregations of individual user ratings, shall be reliable, i.e. should enable a realistic assessment of the probability that the rated party behaves as expected in a transaction. In order for the reputation values to stay reliable and, thus, for the reputation system to provide a benefit, the system needs to be resistant against manipulations by users, the rated parties trying to improve their reputation values, and even against competitors trying to worsen a reputation value. At the same time, a reputation system shall provide privacy protection for users: rated parties shall not be able to learn who provided a certain rating. Otherwise users might not take part in the system as they fear bad feedback in revenge for bad ratings, or users do not want to be connected to certain transactions based on their provided ratings.In this paper we come up with a solution that provides both, reliability of reputation values on the one hand, and privacy protection for users on the other hand. In contrast to related work, our solution only makes use of a single reputation provider that needs to be trusted (to a certain extent) and does not require any bulletin boards to be present in the system. We make use of the Paillier cryptosystem to provide an aggregation of individual user ratings in a way that no party can learn which user provided a certain rating.

For realistic simulations of ad hoc networks, the movements of the participating nodes must be considered. Many models have been proposed which generate movement patterns based on nodes' roles and activities. However, previous models mostly consider node activities individually for each node, making it difficult to precisely model joint activities. While advanced previous models are either based on the notion of schedules or on state machines, we propose a hybrid approach which combines the flexibility of the latter with the ability of the former to express time constraints. Further, as opposed to previous work, our model allows nodes to assume multiple roles and provides an intuitive way to reconcile their potentially conflicting schedules and state machines. Our model also considers the on/off behavior of nodes which is very important to evaluate adaptivity of protocols to node joins and failures under realistic conditions. A major contribution of our work is further that we take into account the fact that mobility, on/off patterns and traffic generation in a network cannot be modeled independently. Our comprehensive model is the first to include all three parts in one consistent form. Finally, it introduces the notion of home and preferred positions which allows very precise modeling of the general node distribution and positioning in an office scenario.