Abstract
In recent years, the problem of data leakage often appears in our lives. As of today, a number of enterprises have been fined heavily for the leakage of user data, including Facebook, Uber and Equifax. This paper makes deep research on the privacy protection of the text. We proposed a three-layer privacy protection mechanism for carrying out privacy-preserving text perturbation. This approach allows different levels of privacy protection for different parts of the text, thereby increasing the level of privacy protection without reducing utility. Extensive experiments prove that the proposed method not only provides fine-grained control over the level of privacy in that data but also improves performance.
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Tang, J., Zhu, T., Xiong, P., Wang, Y., Ren, W. (2020). Privacy and Utility Trade-Off for Textual Analysis via Calibrated Multivariate Perturbations. In: Kutyłowski, M., Zhang, J., Chen, C. (eds) Network and System Security. NSS 2020. Lecture Notes in Computer Science(), vol 12570. Springer, Cham. https://doi.org/10.1007/978-3-030-65745-1_20
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DOI: https://doi.org/10.1007/978-3-030-65745-1_20
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