Corpus ID: 127950074

Obfuscation for Privacy-preserving Syntactic Parsing

@article{Hu2019ObfuscationFP,
  title={Obfuscation for Privacy-preserving Syntactic Parsing},
  author={Zhifeng Hu and Serhii Havrylov and Ivan Titov and Shay B. Cohen},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.09585}
}
  • Zhifeng Hu, Serhii Havrylov, +1 author Shay B. Cohen
  • Published in ArXiv 2019
  • Computer Science, Mathematics
  • The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption. Our primary tool is {\em obfuscation}, relying on the properties of natural language. Specifically, a given text is obfuscated using a neural model that aims to preserve the syntactic relationships of the… CONTINUE READING

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