{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:46:22Z","timestamp":1787017582432,"version":"build-2736575974"},"reference-count":89,"publisher":"Association for Computing Machinery (ACM)","issue":"PLDI","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Program. Lang."],"published-print":{"date-parts":[[2024,6,20]]},"abstract":"<jats:p>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\u00d7 over unencrypted computation) and it is extremely hard to program. Recent hardware accelerators and algorithmic improvements have reduced FHE\u2019s overheads and enabled large applications to run under FHE. These large applications exacerbate FHE\u2019s programmability challenges.<\/jats:p>\n                  <jats:p>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.<\/jats:p>\n                  <jats:p>\n                    We address FHE\u2019s programmability challenges with the Fhelipe FHE compiler. Fhelipe exposes a simple, numpy-style\n                    <jats:italic toggle=\"yes\">tensor<\/jats:italic>\n                    programming interface, and compiles high-level tensor programs into efficient FHE circuits. Fhelipe\u2019s key contribution is\n                    <jats:italic toggle=\"yes\">automatic data packing<\/jats:italic>\n                    , 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.\n                  <\/jats:p>\n                  <jats:p>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\u00d7. At the same time, Fhelipe dramatically simplifies programming, reducing code size by 10\u00d7 \u2013 48\u00d7.<\/jats:p>\n                  <jats:p>\n                    CCS Concepts:\n                    <jats:bold>\u2022 Software and its engineering \u2192 Compilers; \u2022 Security and privacy \u2192 Cryptography.<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1145\/3656382","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T12:27:20Z","timestamp":1718886440000},"page":"126-150","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic Encryption"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-6472","authenticated-orcid":false,"given":"Aleksandar","family":"Krastev","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambrdige, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9188-0717","authenticated-orcid":false,"given":"Nikola","family":"Samardzic","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2408-8441","authenticated-orcid":false,"given":"Simon","family":"Langowski","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8253-7714","authenticated-orcid":false,"given":"Srinivas","family":"Devadas","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2453-2904","authenticated-orcid":false,"given":"Daniel","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"e_1_3_1_2_1","unstructured":"2020. 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