HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits
Jianqiao Mo, Jayanth Gopinath, Brandon Reagen
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d1a86224-9c96-4b14-9d9d-c41da2f96e1aCited by top-tier papers5
- TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor ComputingHusheng Han, Xinyao Zheng, Yuanbo Wen, Yifan Hao et al.ASPLOS 2024 · 12 citations
- Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge ProofsAlhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz et al.ISCA 2025 · 12 citations
- Practical Federated Recommendation Model Learning Using ORAM with Controlled PrivacyJinyu Liu, Wenjie Xiong, G. Edward Suh, Kiwan MaengASPLOS 2025 · 2 citations
- zkPHIRE: A Programmable Accelerator for ZKPs over HIgh-degRee, Expressive GatesAlhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz et al.HPCA 2026 · 1 citation
- PG: Byzantine Fault-Tolerant and Privacy-Preserving Sensor Fusion with Guaranteed Output DeliveryChenglu Jin, Chao Yin, Marten van Dijk, Sisi Duan et al.CCS 2024 · 1 citation
Builds on18
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 2,107 citations
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 1,075 citations
- Oblivious Neural Network Predictions via MiniONN TransformationsJian Liu, Mika Juuti, Yao Lu, N. AsokanCCS 2017 · 800 citations
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas et al.MICRO 2021 · 294 citations
- HEAX: An Architecture for Computing on Encrypted DataM. Sadegh Riazi, Kim Laine, Blake Pelton, Wei DaiASPLOS 2020 · 244 citations
Related papers
- MAGE: Nearly Zero-Cost Virtual Memory for Secure ComputationSam Kumar, David E. Culler, Raluca Ada PopaOSDI 2021 · 24 citations
- PPMLAC: high performance chipset architecture for secure multi-party computationXing Zhou, Zhilei Xu, Cong Wang, Mingyu GaoISCA 2022 · 23 citations
- UFC: A Unified Accelerator for Fully Homomorphic EncryptionMinxuan Zhou, Yujin Nam, Xuan Wang, Youhak Lee et al.MICRO 2024 · 19 citations
- FHE-CGRA: Enable Efficient Acceleration of Fully Homomorphic Encryption on CGRAsMiaomiao Jiang, Yilan Zhu, Honghui You, Cheng Tan et al.DAC 2024 · 5 citations
- AHEC: End-to-end Compiler Framework for Privacy-preserving Machine Learning AccelerationHuili Chen, Rosario Cammarota, Felipe Valencia, Francesco Regazzoni et al.DAC 2020 · 10 citations
