Secure Estimation under Causative Attacks

This paper considers the problem of secure parameter estimation when the estimation algorithm is prone to causative attacks. Causative attacks, in principle, target decision-making algorithms (e.g., inference and learning algorithms) to alter their decisions by making them oblivious to specific attacks. Such attacks influence inference algorithms by tampering with the mechanism through which the algorithm is provided with the statistical model of the population about which an inferential decision is made. Causative attacks are viable, for instance, by contaminating the historical or training data, or by compromising an expert who provides the model. In the presence of causative attacks, the inference algorithms operate under a distorted statistical model for the population from which they collect data samples. This paper introduces specific notions of secure estimation and provides a framework under which secure estimation under causative attacks can be formulated. Closed-form decision rules, and the fundamental tradeoffs between security guarantee and decision qualities are characterized. To circumvent the computational complexity associated with growing parameter dimension or attack complexity, a scalable estimation algorithm and its attendant optimality guarantees are provided.