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Findings of the Association for Computational Linguistics: ACL 2022
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to thedoi:10.18653/v1/2022.findings-acl.277 fatcat:w2m2msqzardynl37jzvjqbbt4q