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TESLAC: Accelerating Lattice-Based Cryptography with AI Accelerator

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Security and Privacy in Communication Networks (SecureComm 2021)

Abstract

In this paper, we exploit AI accelerator to implement cryptographic algorithms. To the best of our knowledge, it is the first attempt to implement quantum-safe Lattice-Based Cryptography (LBC) with AI accelerator. However, AI accelerators are designed for machine learning workloads (e.g., convolution operation), and cannot directly deliver their strong power into the cryptographic computation. Noting that polynomial multiplication over rings is a kind of time-consuming computation in LBC, we utilize a straightforward approach to make the AI accelerator fit well for polynomial multiplication over rings. Additional non-trivial optimizations are also made to minimize the overhead of transformation, such as using low-latency shared memory, coalescing memory access. Moreover, based on NVIDIA AI accelerator, Tensor Core, we have implemented a prototype system named TESLAC and give a set of comprehensive experiments to evaluate its performance. The experimental results show TESLAC can reach tens of millions of operations per second, achieving a performance speedup of two orders of magnitude from the AVX2-accelerated reference implementation. Particularly, with some techniques, TESLAC can also be scaled to other LBC with larger modulo q.

This work was partially supported by National Key R&D Program of China under Award 2018YFB0804401 and National Natural Science Foundation of China under Award No. 61902392.

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Notes

  1. 1.

    NVIDIA has launched different generations of Tensor Core. If there is no additional explanation, Tensor Core in this paper refers to the one on the architecture of Volta.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Lipeng Wan & Fangyu Zheng

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Lipeng Wan

  3. Data Assurance and Communication Security Research Center, Chinese Academy of Sciences, Beijing, China

    Lipeng Wan & Fangyu Zheng

  4. School of Cyber Security, University of Science and Technology of China, Hefei, China

    Jingqiang Lin

Authors
  1. Lipeng Wan
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  2. Fangyu Zheng
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  3. Jingqiang Lin
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Corresponding author

Correspondence to Fangyu Zheng .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France

    Joaquin Garcia-Alfaro

  2. University of Kent Canterbury, Canterbury, Kent, UK

    Shujun Li

  3. University of Washington, Seattle, WA, USA

    Radha Poovendran

  4. Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France

    Hervé Debar

  5. Google Inc., New York, NY, USA

    Moti Yung

A Appendix

A Appendix

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Wan, L., Zheng, F., Lin, J. (2021). TESLAC: Accelerating Lattice-Based Cryptography with AI Accelerator. In: Garcia-Alfaro, J., Li, S., Poovendran, R., Debar, H., Yung, M. (eds) Security and Privacy in Communication Networks. SecureComm 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-90019-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-90019-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90018-2

  • Online ISBN: 978-3-030-90019-9

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