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SecBERT: Privacy-preserving pre-training based neural network inference system.
- Source :
-
Neural Networks . Apr2024, Vol. 172, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Pre-trained models such as BERT have made great achievements in natural language processing tasks in recent years. In this paper, we investigate the privacy-preserving pre-training based neural network inference in a two-server framework based on additive secret sharing technique. Our protocol allows a resource-restrained client to request two powerful servers to cooperatively process the natural processing tasks without revealing any useful information about its data. We first design a series of secure sub-protocols for non-linear functions used in BERT model. These sub-protocols are expected to have broad applications and of independent interest. Based on the building sub-protocols, we propose SecBERT, a privacy-preserving pre-training based neural network inference protocol. SecBERT is the first cryptographically secure privacy-preserving pre-training based neural network inference protocol. We show security, efficiency and accuracy of SecBERT protocol through comprehensive theoretical analysis and experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 172
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 175643439
- Full Text :
- https://doi.org/10.1016/j.neunet.2024.106135