Answers.Body: <p>From <a href="https://aclanthology.org/2022.naacl-main.231.pdf" rel="nofollow noreferrer">Privacy-Preserving Text Classification on BERT Embeddings
  with Homomorphic Encryption</a>:</p>
  <blockquote>
  <p>Research has shown that embeddings can potentially leak private information about sensitive attributes of the text, and in some cases, can be inverted to recover the original input text.</p>
  </blockquote>
  <p>Analyzing the leakage and remediating it is a rather large subfield of research. Here are some other references:</p>
  <ul>
  <li><a href="https://arxiv.org/pdf/2004.00053.pdf" rel="nofollow noreferrer">Information Leakage in Embedding Models</a></li>
  <li><a href="https://arxiv.org/pdf/2211.01141.pdf" rel="nofollow noreferrer">User-Entity Differential Privacy
  in Learning Natural Language Models</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-01-03T23:42:32.413Z
Answers.LastActivityDate: 2024-01-04T10:29:03.030Z
Answers.LastEditDate: 2024-01-04T10:29:03.030Z
Answers.UserDisplayName: Franck Dernoncourt
Body: <p>There is a need of computation that needs to take place, although the person who is sending the raw data is concerned about privacy of such a data.
  <code>Is it possible to gain contextual information or any information just from BERT vector embeddings?</code></p>
  <p>If yes, how can one add <code>salt</code> to the BERT embeddings?</p>
  
Tags: <bert>
Title: Is it possible to convert BERT embeddings to textual format?
UserDisplayName: Vaibhav Maheshwari
ThreadText: # Is it possible to convert BERT embeddings to textual format?
  
  (Asked by: username_0 on 2023-12-30)
  
  There is a need of computation that needs to take place, although the person who is sending the raw data is concerned about privacy of such a data.
  `Is it possible to gain contextual information or any information just from BERT vector embeddings?`
  
  If yes, how can one add `salt` to the BERT embeddings?
  
  **Tags:** bert
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-01-03. Score: 1
  
  From [Privacy-Preserving Text Classification on BERT Embeddings
  with Homomorphic Encryption](https://aclanthology.org/2022.naacl-main.231.pdf):
  
  > Research has shown that embeddings can potentially leak private information about sensitive attributes of the text, and in some cases, can be inverted to recover the original input text.
  
  Analyzing the leakage and remediating it is a rather large subfield of research. Here are some other references:
  
  * [Information Leakage in Embedding Models](https://arxiv.org/pdf/2004.00053.pdf)
  * [User-Entity Differential Privacy
    in Learning Natural Language Models](https://arxiv.org/pdf/2211.01141.pdf)
  
  
  ---
  
  