• Corpus ID: 231942635

Label Leakage and Protection in Two-party Split Learning

@article{Li2021LabelLA,
  title={Label Leakage and Protection in Two-party Split Learning},
  author={Oscar Li and Jiankai Sun and Xin Yang and Weihao Gao and Hongyi Zhang and Junyuan Xie and Virginia Smith and Chong Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.08504}
}
  • Oscar Li, Jiankai Sun, Chong Wang
  • Published 17 February 2021
  • Computer Science
  • ArXiv
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the private label information from the other party during split training, and whether there are methods that can protect against such attacks. Specifically, we first formulate a realistic threat model and propose a privacy loss metric to quantify label leakage in split learning. We then show that there exist two simple yet e… 

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