  Machine learning models have demonstrated promising performance in many
areas. However, the concerns that they can be biased against specific
demographic groups hinder their adoption in high-stake applications. Thus, it
is essential to ensure fairness in machine learning models. Most previous
efforts require direct access to sensitive attributes for mitigating bias.
Nonetheless, it is often infeasible to obtain large-scale users' sensitive
attributes considering users' concerns about privacy in the data collection
process. Privacy mechanisms such as local differential privacy (LDP) are widely
enforced on sensitive information in the data collection stage due to legal
compliance and people's increasing awareness of privacy. Therefore, a critical
problem is how to make fair predictions under privacy. We study a novel and
practical problem of fair classification in a semi-private setting, where most
of the sensitive attributes are private and only a small amount of clean ones
are available. To this end, we propose a novel framework FairSP that can
achieve Fair prediction under the Semi-Private setting. First, FairSP learns to
correct the noise-protected sensitive attributes by exploiting the limited
clean sensitive attributes. Then, it jointly models the corrected and clean
data in an adversarial way for debiasing and prediction. Theoretical analysis
shows that the proposed model can ensure fairness under mild assumptions in the
semi-private setting. Extensive experimental results on real-world datasets
demonstrate the effectiveness of our method for making fair predictions under
privacy and maintaining high accuracy.
