Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads. This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI.

翻译：隐私与安全已迅速成为首要的设计约束。用户现在要求对谁能查看其数据（机密性）以及数据如何使用（控制权）提供更多保护。在此背景下，现有的安全加密技术存在不足：它们能在数据存储或传输时提供保护，但必须在计算时对数据进行解密。幸运的是，存在一种新的计算范式，我们称之为隐私保护计算（PPC）。新兴的PPC技术可用于安全的外包计算，或使两方在不泄露各自秘密数据的情况下进行计算。尽管这些技术在数字时代具有革新用户保护的巨大潜力，但由于其高昂的计算、通信和存储开销，实际应用仍十分有限。本文以神经网络中的私有推理（PI）作为激励应用，回顾了近期在解决各类PPC开销方面的工作。首先，介绍了问题本身及多种技术，包括同态加密（HE）、秘密共享（SS）、混淆电路（GC）和不经意传输（OT）。接着，描述了这些技术在实现PI时所产生的开销特征。这一特征分析揭示了加速GC和HE的必要性。随后介绍了两种解决方案：用于加速GC的HAAC和用于加速HE的RPU。最后，展示了结果与效果，并讨论了为克服PI剩余开销所需开展的未来工作。