MAXelerator: FPGA Accelerator for Privacy Preserving Multiply-Accumulate (MAC) on Cloud Servers
MAXelerator: FPGA Accelerator for Privacy Preserving Multiply-Accumulate (MAC) on Cloud Servers
复制标题
MAXelerator：用于云服务器上隐私保护乘法累加 (MAC) 的 FPGA 加速器
DOI：
10.1145/3195970.3196074
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
F. Koushanfar
中科院分区：
文献类型：
--
作者：
S. Hussain;B. Rouhani;M. Ghasemzadeh;F. Koushanfar
本文介绍了云服务器上的第一个用于隐私机器学习的硬件加速器（ML）。对用户数据的隐私，我们为云服务器上的基于矩阵的ML创建了一个实用的隐私解决方案。隐私敏感的计算归结为矩阵乘法，这是乘数重复（MAC）或MAC本身的重复。与现有的GC框架相比，协议命名为“最大化电路”在隐私敏感的情况下，通过现实世界中的案例研究来证实加速器的有效性。
This paper presents MAXelerator, the first hardware accelerator for privacy-preserving machine learning (ML) on cloud servers. Cloud-based ML is being increasingly employed in various data sensitive scenarios. While it enhances both efficiency and quality of the service, it also raises concern about privacy of the users' data. We create a practical privacy-preserving solution for matrix-based ML on cloud servers. We show that for the majority of the ML applications, the privacy-sensitive computation boils down to either matrix multiplication, which is a repetition of Multiply-Accumulate (MAC) or the MAC itself. We design an FPGA architecture for privacy-preserving MAC to accelerate the ML computation based on the well known Secure Function Evaluation protocol named Yao's Garbled Circuit. MAXelerator demonstrates up to 57 × improvement in throughput per core compared to the fastest existing GC framework. We corroborate the effectiveness of the accelerator with real-world case studies in privacy-sensitive scenarios.