一句话：提出 Coyote 编译器，通过考虑旋转成本优化 FHE 加密算术电路的向量化，避免旋转开销抵消收益。
全同态加密（FHE）允许计算电路直接作用于密文，解密后得到明文计算结果，但性能极慢。FHE 的数学形式支持类 SIMD 执行，因此近期工作尝试用向量化提升性能。然而，现有方法对任意计算效果不佳，因为它们未考虑为让数据参与多个操作而旋转向量操作数的高昂成本，导致旋转开销可能超过向量化收益。Coyote 是面向加密算术电路的编译器，旨在将旋转代价纳入向量化决策，使 FHE 电路在任意计算上也能有效向量化。摘要未报告具体实验评估，但指出该问题可由编译器优化缓解。
Fully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to operate on encrypted data and produce a result that, when decrypted, yields the result of the unencrypted computation. While FHE enables privacy-preserving computation, it is extremely slow. However, the mathematical formulation of FHE supports a SIMD-like execution style, so recent work has turned to vectorization to recover some of the missing performance. Unfortunately, these approaches do not work well for arbitrary computations: they do not account for the high cost of rotating vector operands to allow data to be used in multiple operations. Hence, the cost of rotation can outweigh the benefits of vectorization.