QueryShield: A Platform to Mitigate Enterprise Data Leakage in Queries to External LLMs (2025.naacl-industry)
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Nitin Ramrakhiyani, Delton Myalil, Sachin Pawar, Manoj Apte, Rajan M A, Divyesh Saglani, Imtiyazuddin Shaik
| Challenge: | Unrestricted access to external Large Language Models (LLMs) based services like ChatGPT and Gemini can lead to data leakages, especially for large enterprises providing products and services that require confidentiality guarantees. |
| Approach: | They propose a platform that enterprises can use to query external Large Language Models without leaking confidential internal and client information. |
| Outcome: | The proposed solution minimizes data leakage while limiting impact to semantics while preserving the accuracy of the model candidates. |
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