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We introduce a game of trusted computation in which a sensor equipped withlimited computing power leverages a central node to evaluate a specifiedfunction over a large dataset, collected over time. We assume that the centralcomputer can be under attack and we propose a strategy where the sensor retainsa limited amount of the data to counteract the effect of attack. We formulatethe problem as a two player game in which the sensor (defender) chooses anoptimal fusion strategy using both the non-trusted output from the centralcomputer and locally stored trusted data. The attacker seeks to compromise thecomputation by influencing the fused value through malicious manipulation ofthe data stored on the central node. We first characterize all Nash equilibriaof this game, which turn out to be dependent on parameters known to bothplayers. Next we adopt an Iterated Best Response (IBR) scheme in which, at eachiteration, the central computer reveals its output to the sensor, who thencomputes its best response based on a linear combination of its private localestimate and the untrusted third-party output. We characterize necessary andsufficient conditions for convergence of the IBR along with numerical resultswhich show that the convergence conditions are relatively tight.

Convergence Analysis of Iterated Best Response for a Trusted Computation Game