ABNN2: secure two-party arbitrary-bitwidth quantized neural network predictions
Liyan Shen, Ye Dong, Binxing Fang, Jinqiao Shi, Xuebin Wang, Shengli Pan, Ruisheng Shi
2022Year
11Citations
4Top-tier citations
Abstract
Data privacy and security issues are preventing a lot of potential on-cloud machine learning as services from happening. In the recent past, secure multi-party computation (MPC) has been used to achieve the secure neural network predictions, guaranteeing the privacy of data. However, the cost of the existing two-party solutions is expensive and they are impractical in real-world setting.
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- A Framework for Double-Blind Federated Adaptation of Foundation ModelsNurbek Tastan, Karthik NandakumarICCV 2025
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