The recent rise of privacy concerns has led researchers to devise methods for
private neural inference -- where inferences are made directly on encrypted
data, never seeing inputs. The primary challenge facing private inference is
that computing on encrypted data levies an impractically-high latency penalty,
stemming mostly from non-linear operators like ReLU. Enabling practical and
private inference requires new optimization methods that minimize network ReLU
counts while preserving accuracy. This paper proposes DeepReDuce: a set of
optimizations for the judicious removal of ReLUs to reduce private inference
latency. The key insight is that not all ReLUs contribute equally to accuracy.
We leverage this insight to drop, or remove, ReLUs from classic networks to
significantly reduce inference latency and maintain high accuracy. Given a
target network, DeepReDuce outputs a Pareto frontier of networks that tradeoff
the number of ReLUs and accuracy. Compared to the state-of-the-art for private
inference DeepReDuce improves accuracy and reduces ReLU count by up to 3.5%
(iso-ReLU count) and 3.5$\times$ (iso-accuracy), respectively.

本文提出 DeepReDuce 以降低 ReLU 的总数，从而减少隐私神经推理的延迟时间，保持高精度。与私有推理的最新技术相比，DeepReDuce 在等 RELUs 数量的情况下提高了精度并将 RELUs 数量降低了 3.5％，同时提高了 3.5 倍的精度。

DeepReDuce: 快速私有推理的 ReLU 降维

DeepReDuce: ReLU Reduction for Fast Private Inference

Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary
computations on encrypted data, learning neither the inputs nor the computation
results. Hence, it provides resilience in situations where computations are
carried out by an untrusted or potentially compromised party. This powerful
concept was first conceived by Rivest et al. in the 1970s. However, it remained
unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.
The advent of the massive collection of sensitive data in cloud services,
coupled with a plague of data breaches, moved highly regulated businesses to
increasingly demand confidential and secure computing solutions. This demand,
in turn, has led to a recent surge in the development of FHE tools. To
understand the landscape of recent FHE tool developments, we conduct an
extensive survey and experimental evaluation to explore the current state of
the art and identify areas for future development.
In this paper, we survey, evaluate, and systematize FHE tools and compilers.
We perform experiments to evaluate these tools' performance and usability
aspects on a variety of applications. We conclude with recommendations for
developers intending to develop FHE-based applications and a discussion on
future directions for FHE tools development.

本文介绍了完全同态加密技术（FHE），评估和系统化了 FHE 工具和编译器，并在各种应用程序上评估了这些工具的性能和可用性方面，最后提出了有关 FHE 工具发展的建议和讨论。