Pengzhi Xing (University of Electronic Science and Technology of China), Hongwei Li (University of Electronic Science and Technology of China), Meng Hao (Singapore Management University), Hanxiao Chen (University of Electronic Science and Technology of China), Jia Hu (University of Electronic Science and Technology of China), Dongxiao Liu (University of Electronic Science and Technology of China)

Function Secret Sharing (FSS) has emerged as a pivotal cryptographic tool for secure computation, delivering exceptional online efficiency with constant interaction rounds. However, the reliance on a trusted third party for key generation in existing FSS works compromises both security and practical deployment. In this paper, we introduce efficient distributed key generation schemes for FSS-based distributed point function and distributed comparison function, supporting both input and output to be arithmetic-shared. We further design crucial FSS-based components optimized for online efficiency, serving as the building blocks for advanced protocols. Finally, we propose an efficient framework for evaluating complex trigonometric functions, ubiquitous in scientific computations. Our framework leverages the periodic property of trigonometric functions, which reduces the bit length of input during FSS evaluation. This mitigates the potential performance bottleneck for FSS-based protocols incurred by bit length. Extensive empirical evaluations on real-world applications demonstrate a latency reduction of up to $14.73times$ and a communication cost decrease ranging from $27.67sim 184.42 times$ over the state-of-the-art work.

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Unleashing the Power of Generative Model in Recovering Variable...

Xiangzhe Xu (Purdue University), Zhuo Zhang (Purdue University), Zian Su (Purdue University), Ziyang Huang (Purdue University), Shiwei Feng (Purdue University), Yapeng Ye (Purdue University), Nan Jiang (Purdue University), Danning Xie (Purdue University), Siyuan Cheng (Purdue University), Lin Tan (Purdue University), Xiangyu Zhang (Purdue University)

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URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

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type++: Prohibiting Type Confusion with Inline Type Information

Nicolas Badoux (EPFL), Flavio Toffalini (Ruhr-Universität Bochum, EPFL), Yuseok Jeon (UNIST), Mathias Payer (EPFL)

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