Nishat Koti (IISc Bangalore), Arpita Patra (IISc Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (IISc, Bangalore)

Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four parties with at most one active corruption called Tetrad.

Tetrad works over rings and supports two levels of security, fairness and robustness. The fair multiplication protocol costs 5 ring elements, improving over the state-of-the-art Trident (Chaudhari et al. NDSS'20). A key feature of Tetrad is that robustness comes for free over fair protocols. Other highlights across the two variants include (a) probabilistic truncation without overhead, (b) multi-input multiplication protocols, and (c) conversion protocols to switch between the computational domains, along with a tailor-made garbled circuit approach.

Benchmarking of Tetrad for both training and inference is conducted over deep neural networks such as LeNet and VGG16. We found that Tetrad is up to 4 times faster in ML training and up to 5 times faster in ML inference. Tetrad is also lightweight in terms of deployment cost, costing up to 6 times less than Trident.

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Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators

Shijia Li (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Chunfu Jia (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Pengda Qiu (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data…

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Jinwoo Kim (KAIST), Eduard Marin (Telefonica Research (Spain)), Mauro Conti (University of Padua), Seungwon Shin (KAIST)

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Zhisheng Hu (Baidu Security), Junjie Shen (UC Irvine), Shengjian Guo (Baidu Security), Xinyang Zhang (Baidu Security), Zhenyu Zhong (Baidu Security), Qi Alfred Chen (UC Irvine) and Kang Li (Baidu Security)

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D-Box: DMA-enabled Compartmentalization for Embedded Applications

Alejandro Mera (Northeastern University), Yi Hui Chen (Northeastern University), Ruimin Sun (Northeastern University), Engin Kirda (Northeastern University), Long Lu (Northeastern University)

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