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        <title>Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries</title>
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        <namePart type="given">Yu-Hsiang</namePart>
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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)

• Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries (Huang et al., ACL 2024)

ACL

• Yu-Hsiang Huang, Yuche Tsai, Hsiang Hsiao, Hong-Yi Lin, and Shou-De Lin. 2024. Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4193–4205, Bangkok, Thailand. Association for Computational Linguistics.