  We introduce a game of trusted computation in which a sensor equipped with
limited computing power leverages a central node to evaluate a specified
function over a large dataset, collected over time. We assume that the central
computer can be under attack and we propose a strategy where the sensor retains
a limited amount of the data to counteract the effect of attack. We formulate
the problem as a two player game in which the sensor (defender) chooses an
optimal fusion strategy using both the non-trusted output from the central
computer and locally stored trusted data. The attacker seeks to compromise the
computation by influencing the fused value through malicious manipulation of
the data stored on the central node. We first characterize all Nash equilibria
of this game, which turn out to be dependent on parameters known to both
players. Next we adopt an Iterated Best Response (IBR) scheme in which, at each
iteration, the central computer reveals its output to the sensor, who then
computes its best response based on a linear combination of its private local
estimate and the untrusted third-party output. We characterize necessary and
sufficient conditions for convergence of the IBR along with numerical results
which show that the convergence conditions are relatively tight.
