Secure multi-party computation (MPC) allows users to offload machine learning inference on untrusted servers without having to share their privacy-sensitive data. Despite their strong security properties, MPC-based private inference has not been widely adopted in the real world due to their high communication overhead. When evaluating ReLU layers, MPC protocols incur a significant amount of communication between the parties, making the end-to-end execution time multiple orders slower than its non-private counterpart. This paper presents HummingBird, an MPC framework that reduces the ReLU communication overhead significantly by using only a subset of the bits to evaluate ReLU on a smaller ring. Based on theoretical analyses, HummingBird identifies bits in the secret share that are not crucial for accuracy and excludes them during ReLU evaluation to reduce communication. With its efficient search engine, HummingBird discards 87--91% of the bits during ReLU and still maintains high accuracy. On a real MPC setup involving multiple servers, HummingBird achieves on average 2.03--2.67x end-to-end speedup without introducing any errors, and up to 8.64x average speedup when some amount of accuracy degradation can be tolerated, due to its up to 8.76x communication reduction.

翻译：安全多方计算（MPC）允许用户在不共享隐私敏感数据的情况下，将机器学习推理任务卸载到不可信的服务器上。尽管具有强大的安全属性，基于MPC的私有推理因通信开销过高而在实际应用中尚未广泛采用。在评估ReLU层时，MPC协议需要多方之间的大量通信，导致端到端执行时间比非私有方案慢数个数量级。本文提出HummingBird，一个通过仅使用部分比特在更小环上评估ReLU来显著降低ReLU通信开销的MPC框架。基于理论分析，HummingBird识别出秘密共享中对精度不关键的比特，并在ReLU评估中排除它们以减少通信。借助其高效的搜索引擎，HummingBird在ReLU评估期间舍弃了87–91%的比特，同时仍保持高精度。在涉及多台服务器的真实MPC环境中，HummingBird在不引入任何误差的情况下实现了平均2.03–2.67倍的端到端加速；在可容忍一定精度损失时，由于其高达8.76倍的通信缩减，可实现平均8.64倍的加速。