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\u2019 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\u2019s Garbled Circuit. MAXelerator demonstrates up to 57\u00d7 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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\u2019 data. 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