A Formal Model to Analyze and Compare Reputation Systems for Distributed Networks

A primary difficulty in preventing malicious activity in distributed networks is that nodes do not have fixed classifications. A node’s behavior can fluctuate between generating wanted and unwanted interactions temporally or vary depending on the action involved. To help alleviate this reputation systems have been proposed. However, a formal model which encompasses the prior systems and allows direct comparison has not been forthcoming. We introduce a formal model which encapsulates many prior systems and allows one to determine many important properties of the systems. For example, we are able to derive the necessary and sufficient conditions for a reputation system to bound the successful unwanted interactions a node can generate in its lifetime. We then show that by bounding the unwanted interactions yield a system which maintains an O(1) expected unwanted interactions received per node. To our knowledge these techniques have not been previously shown. We, also, determine the probability of success for a generic routing algorithm. With these tools, we compare the effectiveness of some representative deterministic and probabilistic reputation systems. We then show how to modify these prior systems to improve their effectiveness, and, when possible, how they can be changed to bound the successful unwanted interactions. Our evaluations of the prior trust and routing schemes match our analysis. Our work has many applications, e.g. preventing spams in a social or distributed networks, or in designing a system which is secure against Denial of Service (DoS) attacks.