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• W3164243831 abstract "Ensuring strong theoretical privacy guarantees on text data is a challenging problem which is usually attained at the expense of utility. However, to improve the practicality of privacy preserving text analyses, it is essential to design algorithms that better optimize this tradeoff. To address this challenge, we propose a release mechanism that takes any (text) embedding vector as input and releases a corresponding private vector. The mechanism satisfies an extension of differential privacy to metric spaces. Our idea based on first randomly projecting the vectors to a lower-dimensional space and then adding noise in this projected space generates private vectors that achieve strong theoretical guarantees on its utility. We support our theoretical proofs with empirical experiments on multiple word embedding models and NLP datasets, achieving in some cases more than 10% gains over the existing state-of-the-art privatization techniques." @default.
• W3164243831 created "2021-06-07" @default.
• W3164243831 creator A5021612761 @default.
• W3164243831 creator A5036801391 @default.
• W3164243831 date "2021-01-01" @default.
• W3164243831 modified "2023-09-23" @default.
• W3164243831 title "Private Release of Text Embedding Vectors" @default.
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• W3164243831 doi "https://doi.org/10.18653/v1/2021.trustnlp-1.3" @default.
• W3164243831 hasPublicationYear "2021" @default.
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