{"type":"ScholarlyArticle","title":"Label Leakage in Vertical Federated Learning: A Survey","authors":[{"name":"Yige Liu","slug":"yige-liu","given":"Yige","family":"Liu","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ;"},{"name":"Yiwei Lou","slug":"yiwei-lou","given":"Yiwei","family":"Lou","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ;"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"Institute for AI Industry Research , Tsinghua University , Beijing , China"},{"name":"Yongzhi Cao","slug":"yongzhi-cao","given":"Yongzhi","family":"Cao","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ;; Zhongguancun Laboratory , Beijing , China; School of Computer Science , Peking University , Beijing , China"},{"name":"Hanpin Wang","slug":"hanpin-wang","given":"Hanpin","family":"Wang","affiliation":"Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ;; School of Computer Science and Cyber Engineering , Guangzhou University , Guangzhou , China"}],"abstract":"Vertical federated learning (VFL) is a distributed machine learning paradigm that collaboratively trains models using passive parties with features and an active party with additional labels. While VFL offers privacy preservation through data localization, the threat of label leakage remains a significant challenge. Label leakage occurs due to label inference attacks, where passive parties attempt to infer labels for their privacy and commercial value. Extensive research has been conducted on this specific VFL attack, but a comprehensive summary is still lacking. To bridge this gap, our paper aims to survey the existing label inference attacks and defenses. We propose two new taxonomies for both label inference attacks and defenses, respectively. Beyond summarizing the current state of research, we highlight techniques that we believe hold potential and could significantly influence future studies. Moreover, experimental benchmark datasets and evaluation metrics are summarized to provide a guideline for subsequent work.","doi":"10.24963/ijcai.2024/902","url":"https://ijcai.org/proceedings/2024/902","pdf":"https://ijcai.org/proceedings/2024/0902.pdf","markdown":"https://ijcai.org/proceedings/2024/902.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/902","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":902,"pages":{"first":8160,"last":8169},"keywords":["Machine Learning: ML: Federated learning","Multidisciplinary Topics and Applications: MTA: Security and privacy"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martin Abadi, Andy Chu, Ian Goodfel- low, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 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