Summary Text Embeddings and Private Information Leakage arxiv.org
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The Vec2Text method corrects and re-embeds text inputs, recovering 92% of them, while also defending against inversion attacks but having scalability limitations.
Slides
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Key Points
- Text embeddings can reveal private information about the original text.
- The study investigates the problem of embedding inversion and proposes a method called Vec2Text to reconstruct the full text from dense text embeddings.
- Vec2Text can recover 92% of 32-token text inputs exactly through a multi-step approach.
- Large language models store auxiliary data in dense embeddings, which pose privacy threats.
- The authors frame the problem of recovering textual embeddings as a controlled generation problem and evaluate their method on various retrieval corpuses.
- Gaussian noise can be added to embeddings as a defense mechanism against inversion attacks.
- The scalability of the method to longer text, adversary access to the model, search thoroughness, and impact of word frequency are limitations of the study.
- Text embeddings should be treated as highly sensitive private data and protected accordingly.
Summaries
21 word summary
Vec2Text method recovers 92% of text inputs by correcting and re-embedding text. It defends against inversion attacks but has scalability limitations.
62 word summary
The study introduces Vec2Text, a method for reconstructing text from text embeddings. It can recover 92% of 32-token text inputs by iteratively correcting and re-embedding the text. The authors evaluate Vec2Text on embeddings from different retrieval corpuses and propose a defense mechanism against inversion attacks. Limitations include scalability and assumptions about adversary access. The study emphasizes the privacy implications of text embeddings.
177 word summary
The study investigates the problem of embedding inversion, which involves reconstructing the original text from text embeddings. The authors propose a method called Vec2Text, which aims to generate text that closely matches a given embedding. The model can recover 92% of 32-token text inputs exactly by iteratively correcting and re-embedding the text. The study focuses on the privacy threats associated with large language models that store auxiliary data in vector databases of dense embeddings. The authors present Vec2Text as a solution and evaluate it on embeddings generated from various retrieval corpuses, successfully recovering the inputs for multiple datapoints across different domains. The authors also consider the privacy implications of dense text embeddings and propose a defense mechanism against inversion attacks by adding Gaussian noise to the embeddings. The study acknowledges limitations such as scalability to longer texts, assumptions about adversary access, and the need for further study on search thoroughness and impact of word frequency. In conclusion, the study emphasizes the privacy implications of text embeddings and highlights the need to treat them as sensitive private data.
410 word summary
This study explores the problem of embedding inversion, which involves reconstructing the original text from dense text embeddings. The authors introduce a method called Vec2Text, which aims to generate text that closely matches a given embedding. Through a multi-step approach, the model can recover 92% of 32-token text inputs exactly by iteratively correcting and re-embedding the text.
The study focuses on large language models that store auxiliary data in vector databases of dense embeddings. These databases are commonly used for efficient embedding searches. However, the privacy threats associated with these databases have not been extensively investigated. The authors question whether a third-party service can reproduce the original text based on its embedding. While neural networks are typically difficult to invert exactly, it is often possible to approximate their inverse based on input-output pairs from the network. This study specifically targets the full reconstruction of input text from its embedding.
To address this problem, the authors present Vec2Text as a solution. This method leverages the difference between a hypothesis embedding and a ground-truth embedding to make discrete updates to the text hypothesis. The model is trained on datasets of texts and embeddings, learning to generate text that closely matches a given embedding. The authors evaluate Vec2Text on embeddings generated from various retrieval corpuses and find that it successfully recovers the inputs for multiple datapoints across different domains.
In terms of experimental setup, the authors train their models on different datasets and evaluate them using metrics such as BLEU score, Token F1, and exact match. They also consider the privacy implications of dense text embeddings and propose a defense mechanism against inversion attacks by adding Gaussian noise to the embeddings. Results show that this approach effectively defends against naive inversion attacks while preserving utility in the nearest-neighbor retrieval setting.
However, the study has limitations. The scalability of Vec2Text to longer texts has not been thoroughly explored. The authors also assume that the adversary has black-box access to the model used for generating the embeddings, which may not be realistic in all scenarios. Additionally, the search thoroughness and the impact of word frequency on model correctness have not been extensively studied.
In conclusion, this study highlights that text embeddings can expose significant private information about the original text. The Vec2Text method demonstrates the ability to recover text from its embedding, emphasizing the privacy implications of text embeddings. The findings suggest that embeddings should be treated as highly sensitive private data and protected accordingly.
482 word summary
Text embeddings can reveal a significant amount of private information about the original text. This study investigates the problem of embedding inversion, which involves reconstructing the full text from dense text embeddings. The authors propose a method called Vec2Text, which aims to generate text that is close to a given embedding. They find that a multi-step approach that iteratively corrects and re-embeds text can recover 92% of 32-token text inputs exactly. The model is trained to decode text embeddings from two state-of-the-art embedding models and is also able to recover important personal information, such as full names, from a dataset of clinical notes.
Large language models often store auxiliary data in a vector database of dense embeddings. These databases are popular for efficient embedding searches at scale. However, the privacy threats within these databases have not been extensively explored. Can a third-party service reproduce the original text given its embedding? While neural networks are generally difficult to invert exactly, it is often possible to approximate their inverse given input-output pairs from the network. Previous work has explored this question for images and shallow networks, but this study targets full reconstruction of input text from its embedding.
The authors frame the problem of recovering textual embeddings as a controlled generation problem. Their method, Vec2Text, uses the difference between a hypothesis embedding and a ground-truth embedding to make discrete updates to the text hypothesis. The model is trained on datasets of texts and embeddings and learns to generate text that is as close as possible to a given embedding. The authors evaluate their method on embeddings generated from various retrieval corpuses and find that it can recover the inputs for a number of datapoints across different domains.
In terms of experimental setup, the authors train their models on different datasets and evaluate them using various metrics such as BLEU score, Token F1, and exact match. They also consider the privacy implications of dense text embeddings and propose adding Gaussian noise to the embeddings as a defense mechanism against inversion attacks. The results show that adding a small amount of noise can effectively defend against naive inversion attacks while still preserving utility in the nearest-neighbor retrieval setting.
The study has several limitations. The scalability of the method to longer text has not been thoroughly investigated. The authors also assume that the adversary has black-box access to the model used to generate the embeddings, which may not be realistic in all scenarios. Additionally, the search thoroughness and the impact of word frequency on model correctness have not been extensively studied.
In conclusion, text embeddings can reveal a significant amount of private information about the original text. The Vec2Text method proposed in this study demonstrates the ability to recover text from its embedding and highlights the privacy implications of text embeddings. The findings suggest that embeddings should be treated as highly sensitive private data and protected accordingly.