Fully Homomorphic Encryption (FHE) enables computing on encrypted data, letting clients securely offload computation to untrusted servers. While enticing, FHE has two key challenges that limit its applicability: it has high performance overheads (10,000× over unencrypted computation) and it is extremely hard to program. Recent hardware accelerators and algorithmic improvements have reduced FHE’s overheads and enabled large applications to run under FHE. These large applications exacerbate FHE’s programmability challenges. Writing FHE programs directly is hard because FHE schemes expose a restrictive, low-level interface that prevents abstraction and composition. Specifically, FHE requires packing encrypted data into large vectors (tens of thousands of elements long), FHE provides limited operations on these vectors, and values have noise that grows with each operation, which creates unintuitive performance tradeoffs. As a result, translating large applications, like neural networks, into efficient FHE circuits takes substantial tedious work. We address FHE’s programmability challenges with the Fhelipe FHE compiler. Fhelipe exposes a simple, numpy-style tensor programming interface, and compiles high-level tensor programs into efficient FHE circuits. Fhelipe’s key contribution is automatic data packing , which chooses data layouts for tensors and packs them into ciphertexts to maximize performance. Our novel framework considers a wide range of layouts and optimizes them analytically. This lets Fhelipe compile large FHE programs efficiently, unlike prior FHE compilers, which either use inefficient layouts or do not scale beyond tiny programs. We evaluate Fhelipe on both a state-of-the-art FHE accelerator and a CPU. Fhelipe is the first compiler that matches or exceeds the performance of large hand-optimized FHE applications, like deep neural networks, and outperforms a state-of-the-art FHE compiler by gmean 18.5×. At the same time, Fhelipe dramatically simplifies programming, reducing code size by 10× – 48×. CCS Concepts: • Software and its engineering → Compilers; • Security and privacy → Cryptography.

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000X over unencrypted computation. Recent FHE accelerators have made strides to bridge this performance gap. Unfortunately, prior accelerators only work well for simple programs, but become inefficient for complex programs, which bring additional costs and challenges.

Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE’s widespread adoption.