Text Embeddings Reveal (Almost) As Much As Text (2310.06816v1)

Abstract: How much private information do text embeddings reveal about the original text? We investigate the problem of embedding \textit{inversion}, reconstructing the full text represented in dense text embeddings. We frame the problem as controlled generation: generating text that, when reembedded, is close to a fixed point in latent space. We find that although a na\"ive model conditioned on the embedding performs poorly, a multi-step method that iteratively corrects and re-embeds text is able to recover $92\%$ of $32\text{-token}$ text inputs exactly. We train our model to decode text embeddings from two state-of-the-art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes. Our code is available on Github: \href{https://github.com/jxmorris12/vec2text}{github.com/jxmorris12/vec2text}.

• The paper introduces a controlled generation framework using Vec2Text, achieving 92% exact recovery of 32-token texts.
• Empirical evaluations show high in-domain BLEU scores of 97.3 and cosine similarities over 0.99 across multiple datasets.
• The study reveals critical privacy concerns, demonstrating that embeddings can expose sensitive clinical information with high accuracy.

Overview of the Paper on Embedding Inversion

The paper "Text Embeddings Reveal (Almost) As Much As Text" by John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M. Rush presents a comprehensive exploration of embedding inversion, focusing on the reconstruction of full text from dense text embeddings. The authors introduce a novel approach, Vec2Text, which iteratively corrects and re-embeds text hypotheses to align closely with a target embedding in the latent space.

Core Contributions

The primary contributions of the paper are as follows:

1. Controlled Generation Framework: The authors frame embedding inversion as a controlled generation problem, wherein the aim is to generate text such that its embedding closely matches a given target embedding. This contrasts with naive models that condition on embeddings but perform poorly.
2. Vec2Text Method: A multi-step method, Vec2Text, is proposed, which can iteratively refine text hypotheses. The method significantly outperforms naive approaches and achieves a high degree of accuracy in text reconstruction.
3. Empirical Evaluation: The paper demonstrates the efficacy of Vec2Text across multiple datasets and embedding models. Notably, 92% of 32-token text inputs are recovered exactly with near-perfect BLEU scores.
4. Privacy Implications: The authors highlight substantial privacy concerns by showing that text embeddings can leak significant amounts of sensitive information. Specifically, they demonstrate that clinical notes can be de-anonymized with high accuracy, recovering 89% of full names from embeddings in the MIMIC-III dataset.

Numerical Results and Analysis

The paper provides robust empirical evidence of the effectiveness of the Vec2Text approach. Key results include:

• In-Domain Performance: On in-domain datasets, Vec2Text achieves a BLEU score of 97.3 and exact text recovery in 92% of cases for 32-token sequences. The cosine similarity between original and recovered embeddings consistently exceeds 0.99.
• Out-of-Domain Performance: Vec2Text shows strong performance across various datasets from the BEIR benchmark, maintaining high Token F1 scores and cosine similarities despite differences in text length and domain.
• Clinical Data: In the clinical domain, Vec2Text reconstructs up to 94% of first names and 95% of last names from embedded 32-token clinical notes, illustrating the method's capability in extracting sensitive information.

Practical and Theoretical Implications

The findings of this paper have significant implications for both practice and theory:

1. Data Privacy: The clear demonstration that text embeddings can be inverted to reveal original text underscores the need for treating embeddings with the same privacy considerations as raw text. This has critical implications for handling sensitive information in fields like healthcare and finance.
2. Embedding Security: The notion that embeddings can act almost as direct proxies for raw data necessitates the development of more robust privacy-preserving techniques. Adding noise to embeddings is one potential defense mechanism, albeit with trade-offs in retrieval performance.
3. Future Research Directions:
   • Improving Robustness: Future work could explore advanced adversarial training methods to strengthen the robustness of embeddings against inversion attacks.
   • Scalability: Extending the applicability of Vec2Text to longer text sequences and more complex embeddings could further elucidate the boundaries of this method.
   • Real-World Applications: Investigating the integration of Vec2Text with existing retrieval and classification systems in real-world applications can provide practical insights and additional layers of security.

Conclusion

The paper robustly establishes that text embeddings can be effectively inverted using iterative correction methods such as Vec2Text. The results indicate that embeddings, while useful for many NLP tasks, carry intrinsic privacy risks similar to raw text data. Thus, embedding models and systems utilizing such embeddings must incorporate stringent privacy safeguards to mitigate potential information leakage. This work fundamentally challenges the perception of embeddings being abstract, non-invertible representations, opening a new avenue for securing text representation in NLP applications.