AI 中文总结
本文提出分布式函数加速器（DFA），针对FSS的核心原语DPF优化，在不可信/可信模式下分别提升性能、降低开销，在安全推理和PIR场景中实现显著性能提升且硬件成本适中。
AI 中文摘要
函数秘密共享（FSS）是隐私保护系统（如安全推理和私有信息检索（PIR））的核心构建模块，但在密钥生成、通信和数据处理方面会产生显著开销。本文提出分布式函数加速器（DFA），这是一种针对FSS中主要原语——分布式点函数（DPF）生成与评估的硬件加速器。DFA结合了用于AES基伪随机数生成的高吞吐量固定功能引擎，以及用于协议特定逻辑的轻量可编程单元。在不可信模式下，DFA作为DPF评估的纯加速器，在不改变协议的情况下提升吞吐量和能效；在可信模式下，它还支持本地即时密钥生成，消除密钥分发需求，减少存储和数据移动开销。在代表性工作负载中，DFA使安全推理的端到端延迟降低10倍、通信开销最高降低20倍、能耗节省超5倍；使PIR的吞吐量提升5倍、能耗降低10倍，且硬件成本适中。
英文摘要
Function secret sharing (FSS) is a core building block for privacy-preserving systems such as secure inference and private information retrieval (PIR), but incurs significant overhead in key generation, communication, and data movement.We present the distributed function accelerator (DFA), a hardware accelerator that targets the dominant primitive in FSS: distributed point function (DPF) generation and evaluation. DFA combines a high-throughput fixed-function engine for AES-based pseudorandom number generation with a lightweight programmable unit for protocol-specific logic. In untrusted mode, DFA serves as a pure accelerator for DPF evaluation, improving throughput and energy efficiency without changing the protocol. In trusted mode, it further enables local, on-the-fly key generation, eliminating key distribution, and reducing storage and data movement overheads. Across representative workloads, DFA achieves a reduction of 10X end-to-end latency, a reduction of up to 20X communication and more than 5X energy savings for secure inference; and an improvement of 5X throughput and 10X energy reduction for PIR, with modest hardware cost.
