The collaboration of financial institutes against fraudsters is a promising path for reducing resource investments and increasing coverage. Yet, such collaboration is held back by two somewhat conflicting challenges: effective knowledge sharing and limiting leakage of private information. While the censorship of private information is likely to reduce knowledge sharing effectiveness, the generalization of private information to a desired degree can potentially allow, on one hand, to limit the leakage, and on the other hand, to reveal some properties of the private information that can be beneficial for sharing.

Following [4] we extend and generalize the game-theoretic model of distributed computing, identifying different utility functions that encompass different potential preferences of players in a distributed system. A good distributed algorithm in the game-theoretic context is one that prohibits the agents (processors with interests) from deviating from the protocol; any deviation would result in the agent losing, i.e., reducing its utility at the end of the algorithm. We distinguish between different utility functions in the context of distributed algorithms, e.g., utilities based on communication preference, solution preference, and output preference. Given these preferences we construct two basic building blocks for game theoretic distributed algorithms, a wake-up building block resilient to any preference and in particular to the communication preference (to which previous wake-up solutions were not resilient), and a knowledge sharing building block that is resilient to any and in particular to solution and output preferences. Using the building blocks we present several new algorithms for consensus, and renaming as well as a modular presentation of the leader election algorithm of [4].