Differentially Private Counterfactuals via Functional Mechanism

Such advantage enables humans to conduct reasoning under the "what-if" circumstances, and shows the Counterfactual, serving as one emerging type of model explanation, potential actions to take for altering model decisions. Essentially, has attracted tons of attentions recently from both industry and counterfactuals are hypothetical data samples synthesized within academia. Different from the conventional feature-based explanations certain distributions, which may not necessarily exist in the real (e.g., attributions), counterfactuals are a series of hypothetical world. Given a loan rejection case for example, valid counterfactuals samples which can flip model decisions with minimal perturbations would suggest some specific changes on the profile to make on queries. Given valid counterfactuals, humans are capable of reasoning it approved, such as increasing annual income from $60, 000 to under "what-if" circumstances, so as to better understand $80, 000 or improving education level from High-School to College. the model decision boundaries. However, releasing counterfactuals Nevertheless, releasing model explanations could be risky, since could be detrimental, since it may unintentionally leak sensitive meaningful explanations always contain additional information on information to adversaries, which brings about higher risks on how model works. Adversaries may purposely collect those released both model security and data privacy. To bridge the gap, in this explanations to infer model properties, which can cause serious paper, we propose a novel framework to generate differentially issues on both model security [22] and data privacy [42]. Counterfactual private counterfactual (DPC) without touching the deployed model explanation can make such threat even more significant, or explanation set, where noises are injected for protection while considering the fact that it directly reveals the decision boundaries maintaining the explanation roles of counterfactual.