Paper 2022/892

Piranha: A GPU Platform for Secure Computation

Jean-Luc Watson, University of California, Berkeley
Sameer Wagh, University of California, Berkeley, Devron Corporation
Raluca Ada Popa, University of California, Berkeley

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine learning (ML). However, secure training of large-scale ML models currently requires a prohibitively long time to complete. Given that large ML inference and training tasks in the plaintext setting are significantly accelerated by Graphical Processing Units (GPUs), this raises the natural question: can secure MPC leverage GPU acceleration? A few recent works have studied this question in the context of accelerating specific components or protocols, but do not provide a general-purpose solution. Consequently, MPC developers must be both experts in cryptographic protocol design and proficient at low-level GPU kernel development to achieve good performance on any new protocol implementation. We present Piranha, a general-purpose, modular platform for accelerating secret sharing-based MPC protocols using GPUs. Piranha allows the MPC community to easily leverage the benefits of a GPU without requiring GPU expertise. Piranha contributes a three-layer architecture: (1) a device layer that can independently accelerate secret-sharing protocols by providing integer-based kernels absent in current general-purpose GPU libraries, (2) a modular protocol layer that allows developers to maximize utility of limited GPU memory with in-place computation and iterator-based support for non-standard memory access patterns, and (3) an application layer that allows applications to remain completely agnostic to the underlying protocols they use. To demonstrate the benefits of Piranha, we implement 3 state-of-the-art linear secret sharing MPC protocols for secure NN training: 2-party SecureML (IEEE S&P ’17), 3-party Falcon (PETS ’21), and 4-party FantasticFour (USENIX Security ’21). Compared to their CPU-based implementations, the same protocols implemented on top of Piranha’s protocol-agnostic acceleration exhibit a 16−48× decrease in training time. For the first time, Piranha demonstrates the feasibility of training a realistic neural network (e.g. VGG), end-to-end, using MPC in a little over one day. Piranha is open source and available at

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Publication info
Published elsewhere. USENIX Security '22
Multi-party computation GPU acceleration Privacy-preserving ML
Contact author(s)
jlw @ berkeley edu
2022-08-26: last of 3 revisions
2022-07-07: received
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      author = {Jean-Luc Watson and Sameer Wagh and Raluca Ada Popa},
      title = {Piranha: A GPU Platform for Secure Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2022/892},
      year = {2022},
      note = {\url{}},
      url = {}
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