Multi-label Learning from Privacy-Label
Zhongnian Li Haotian Ren Tongfeng Sun Zhichen Li
Multi-abel Learning (MLL) often involves the assignment of multiple relevant
labels to each instance, which can lead to the leakage of sensitive information
(such as smoking, diseases, etc.) about the instances. However, existing MLL
suffer from failures in protection for sensitive information. In this paper, we
propose a novel setting named Multi-Label Learning from Privacy-Label (MLLPL),
which Concealing Labels via Privacy-Label Unit (CLPLU). Specifically, during
the labeling phase, each privacy-label is randomly combined with a non-privacy
label to form a Privacy-Label Unit (PLU). If any label within a PLU is
positive, the unit is labeled as positive; otherwise, it is labeled negative,
as shown in Figure 1. PLU ensures that only non-privacy labels are appear in
the label set, while the privacy-labels remain concealed. Moreover, we further
propose a Privacy-Label Unit Loss (PLUL) to learn the optimal classifier by
minimizing the empirical risk of PLU. Experimental results on multiple
benchmark datasets demonstrate the effectiveness and superiority of the
proposed method.