{"type":"ScholarlyArticle","title":"Protecting Split Learning by Potential Energy Loss","authors":[{"name":"Fei Zheng","slug":"fei-zheng","given":"Fei","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Lingjuan Lyu","slug":"lingjuan-lyu","given":"Lingjuan","family":"Lyu","affiliation":"Sony AI"},{"name":"Xinyi Fu","slug":"xinyi-fu","given":"Xinyi","family":"Fu","affiliation":"Ant Group"},{"name":"Xing Fu","slug":"xing-fu","given":"Xing","family":"Fu","affiliation":"Ant Group"},{"name":"Weiqiang Wang","slug":"weiqiang-wang","given":"Weiqiang","family":"Wang","affiliation":"Ant Group"},{"name":"Xiaolin Zheng","slug":"xiaolin-zheng","given":"Xiaolin","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Jianwei Yin","slug":"jianwei-yin","given":"Jianwei","family":"Yin","affiliation":"College of Computer Science and Technology , Zhejiang University"}],"abstract":"As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the intermediate results are shared during training and inference. In this paper, we focus on the privacy leakage from the forward embeddings of split learning. Specifically, since the forward embeddings contain too much information about the label, the attacker can either use a few labeled samples to fine-tune the top model or perform unsupervised attacks such as clustering to infer the true labels from the forward embeddings. To prevent such kind of privacy leakage, we propose the potential energy loss to make the forward embeddings more 'complicated', by pushing embeddings of the same class towards the decision boundary. Therefore, it is hard for the attacker to learn from the forward embeddings. Experiment results show that our method significantly lowers the performance of both fine-tuning attacks and clustering attacks.","doi":"10.24963/ijcai.2024/618","url":"https://ijcai.org/proceedings/2024/618","pdf":"https://ijcai.org/proceedings/2024/0618.pdf","markdown":"https://ijcai.org/proceedings/2024/618.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/618","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":"Machine Learning","track":"Main Track","number":618,"pages":{"first":5590,"last":5598},"keywords":["AI Ethics, Trust, Fairness: ETF: Safety and robustness","Machine Learning: ML: Federated learning","Multidisciplinary Topics and Applications: MTA: Security and privacy"],"videos":[],"references":[{"position":1,"raw":"[Abuadbba et al., 2020] Sharif Abuadbba, Kyuyeon Kim, Minki Kim, Chandra Thapa, Seyit Ahmet C ¸amtepe, Yan- song Gao, Hyoungshick Kim, and Surya Nepal. 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