In recent years, a growing body of work has emerged on how to learn machine
learning models under fairness constraints, often expressed with respect to
some sensitive attributes. In this work, we consider the setting in which an
adversary has black-box access to a target model and show that information
about this model's fairness can be exploited by the adversary to enhance his
reconstruction of the sensitive attributes of the training data. More
precisely, we propose a generic reconstruction correction method, which takes
as input an initial guess made by the adversary and corrects it to comply with
some user-defined constraints (such as the fairness information) while
minimizing the changes in the adversary's guess. The proposed method is
agnostic to the type of target model, the fairness-aware learning method as
well as the auxiliary knowledge of the adversary. To assess the applicability
of our approach, we have conducted a thorough experimental evaluation on two
state-of-the-art fair learning methods, using four different fairness metrics
with a wide range of tolerances and with three datasets of diverse sizes and
sensitive attributes. The experimental results demonstrate the effectiveness of
the proposed approach to improve the reconstruction of the sensitive attributes
of the training set.

本篇论文提出了一种通用的重构校正方法，可以在满足用户定义的约束条件（如公平性信息）的同时最小化敌手的猜测的更改，并解决了黑盒访问目标模型的情况下反应了培训数据敏感属性的问题。