@inproceedings{huang-etal-2024-transferable,
title = "Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries",
author = "Huang, Yu-Hsiang and
Tsai, Yuche and
Hsiao, Hsiang and
Lin, Hong-Yi and
Lin, Shou-De",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.230",
pages = "4193--4205",
abstract = "This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model{'}s behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures.",
}
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<abstract>This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model’s behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures.</abstract>
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%0 Conference Proceedings
%T Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
%A Huang, Yu-Hsiang
%A Tsai, Yuche
%A Hsiao, Hsiang
%A Lin, Hong-Yi
%A Lin, Shou-De
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F huang-etal-2024-transferable
%X This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model’s behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures.
%U https://aclanthology.org/2024.acl-long.230
%P 4193-4205
Markdown (Informal)
[Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries](https://aclanthology.org/2024.acl-long.230) (Huang et al., ACL 2024)
ACL