Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

While neural networks (NNs) are traditionally associated with tasks such as image recognition and natural language processing, this paper presents a novel application of NNs for efficient cryptographic computations. Leveraging the Turing completeness and inherent adaptability of NN models, we propose a transformative approach that efficiently accelerates cryptographic computations on various platforms. More specifically, with a program translation framework that converts traditional cryptographic algorithms into NN models, our proof-of-concept implementations in TensorFlow demonstrate substantial performance improvements: encryption speeds for AES, Chacha20, and Salsa20 show increases of up to 4.09$times$, 5.44$times$, and 5.06$times$, respectively, compared to existing GPU-based cryptographic solutions written by human experts. These enhancements are achieved without compromising the security of the original cryptographic algorithms, ensuring that our neural network-based approach maintains robust security standards. This repurposing of NNs opens new pathways for the development of scalable, efficient, and secure cryptographic systems that can adapt to the evolving
demands of modern computing environments.

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CCTAG: Configurable and Combinable Tagged Architecture

Zhanpeng Liu (Peking University), Yi Rong (Tsinghua University), Chenyang Li (Peking University), Wende Tan (Tsinghua University), Yuan Li (Zhongguancun Laboratory), Xinhui Han (Peking University), Songtao Yang (Zhongguancun Laboratory), Chao Zhang (Tsinghua University)

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Enhancing Security in Third-Party Library Reuse – Comprehensive Detection...

Shangzhi Xu (The University of New South Wales), Jialiang Dong (The University of New South Wales), Weiting Cai (Delft University of Technology), Juanru Li (Feiyu Tech), Arash Shaghaghi (The University of New South Wales), Nan Sun (The University of New South Wales), Siqi Ma (The University of New South Wales)

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Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

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LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

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