Privacy Preserving Plans in Partially Observable Environments

Big brother is watching but his eyesight is not all that great, since he only has partial observability of the environment. In such a setting agents may be able to preserve their privacy by hiding their true goal, following paths that may lead to multiple goals. In this work we present a framework that supports the offline analysis of goal recognition settings with non-deterministic system sensor models, in which the observer has partial (and possibly noisy) observability of the agent's actions, while the agent is assumed to have full observability of his environment. In particular, we propose a new variation of worst case distinctiveness (wcd), a measure that assesses the ability to perform goal recognition within a model. We describe a new efficient way to compute this measure via a novel compilation to classical planning. In addition, we discuss the tools agents have to preserve privacy, by keeping their goal ambiguous as long as possible. Our empirical evaluation shows the feasibility of the proposed solution.