# Multi-label Learning from Privacy-Label

arXiv: https://arxiv.org/abs/2312.13312 · HTML: https://mlsanity.com/paper/2312.13312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement](https://mlsanity.com/paper/2206.00517/) (2022)
- [Deep Multi-Species Embedding](https://mlsanity.com/paper/1609.09353/) (2016)
- [Multi-label Learning with Missing Labels using Mixed Dependency Graphs](https://mlsanity.com/paper/1804.00117/) (2018)
- [HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders](https://mlsanity.com/paper/2103.06375/) (2021)
- [Semantic Representation and Dependency Learning for Multi-Label Image Recognition](https://mlsanity.com/paper/2204.03795/) (2022)
- [Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels](https://mlsanity.com/paper/2209.01314/) (2022)

## Abstract (arXiv)

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.