• Corpus ID: 237420397

Increasing Adversarial Uncertainty to Scale Private Similarity Testing

@article{Hua2021IncreasingAU,
  title={Increasing Adversarial Uncertainty to Scale Private Similarity Testing},
  author={Yiqing Hua and Armin Namavari and Kai-Wen Cheng and Mor Naaman and Thomas Ristenpart},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.01727}
}
Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. However, this method fundamentally endangers user privacy. Instead, we target client-side detection, notifying only the users when such matches occur to warn them against abusive content. Our solution is based on privacy-preserving similarity testing… 

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