HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits
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Abstract (arXiv)
Privacy and security have rapidly emerged as priorities in system design. One powerful solution for providing both is privacy-preserving computation, where functions are computed directly on encrypted data and control can be provided over how data is used. Garbled circuits (GCs) are a PPC technology that provide both confidential computing and control over how data is used. The challenge is that they incur significant performance overheads compared to plaintext. This paper proposes a novel garbled circuits accelerator and compiler, named HAAC, to mitigate performance overheads and make privacy-preserving computation more practical. HAAC is a hardware-software co-design. GCs are exemplars of co-design as programs are completely known at compile time, i.e., all dependence, memory accesses, and control flow are fixed. The design philosophy of HAAC is to keep hardware simple and efficient, maximizing area devoted to our proposed custom execution units and other circuits essential for high performance (e.g., on-chip storage). The compiler can leverage its program understanding to realize hardware's performance potential by generating effective instruction schedules, data layouts, and orchestrating off-chip events. In taking this approach we can achieve ASIC performance/efficiency without sacrificing generality. Insights of our approach include how co-design enables expressing arbitrary GCs programs as streams, which simplifies hardware and enables complete memory-compute decoupling, and the development of a scratchpad that captures data reuse by tracking program execution, eliminating the need for costly hardware managed caches and tagging logic. We evaluate HAAC with VIP-Bench and achieve an average speedup of 589× with DDR4 (2,627× with HBM2) in 4.3mm2 of area.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2022.
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Citing papers per year: 2023: 2; 2024: 3.
5 papers.
- 2023PriViT: Vision Transformers for Fast Private Inferencecited by 4
- 2023Towards Fast and Scalable Private Inferencecited by 1
- 2024TruncFormer: Private LLM Inference Using Only Truncationscited by 1
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computingcited by 0
- 2024Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deploymentcited by 0
- 2024TruncFormer: Private LLM Inference Using Only Truncations2 Dec 2024
- 2024Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment29 Oct 2024
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing12 Jul 2024
- 2023PriViT: Vision Transformers for Fast Private Inference6 Oct 2023
- 2023Towards Fast and Scalable Private Inference9 Jul 2023