{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-neural-networks-for-encrypted-inference","title":"Deep Neural Networks for Encrypted Inference with TFHE","arxiv_id":"2302.10906","date":"2023-02-13","proceeding":null,"authors":["Andrei Stoian","Jordan Frery","Roman Bredehoft","Luis Montero","Celia Kherfallah","Benoit Chevallier-Mames"],"abstract":"Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption. FHE preserves the privacy of the users of online services that handle sensitive data, such as health data, biometrics, credit scores and other personal information. A common way to provide a valuable service on such data is through machine learning and, at this time, Neural Networks are the dominant machine learning model for unstructured data. In this work we show how to construct Deep Neural Networks (DNN) that are compatible with the constraints of TFHE, an FHE scheme that allows arbitrary depth computation circuits. We discuss the constraints and show the architecture of DNNs for two computer vision tasks. We benchmark the architectures using the Concrete stack, an open-source implementation of TFHE.","url_abs":"https://arxiv.org/abs/2302.10906v1","url_pdf":"https://arxiv.org/pdf/2302.10906v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-neural-networks-for-encrypted-inference","repo_url":"https://github.com/zama-ai/concrete-ml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[{"method_slug":null,"method_name":null}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.10906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10906"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zama-ai/concrete-ml","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"d529c9d74606cd8b","entry":"get_torchvision_dataset","repo":"zama-ai/concrete-ml","repo_kind":"official","path":"use_case_examples/cifar/cifar_brevitas_finetuning/cifar_utils.py","file_url":"https://github.com/zama-ai/concrete-ml/blob/HEAD/use_case_examples/cifar/cifar_brevitas_finetuning/cifar_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"d529c9d74606cd8b"}},{"code_sha256_prefix":"292cfbcfee9ee087","entry":"train","repo":"zama-ai/concrete-ml","repo_kind":"official","path":"use_case_examples/cifar/cifar_brevitas_finetuning/cifar_utils.py","file_url":"https://github.com/zama-ai/concrete-ml/blob/HEAD/use_case_examples/cifar/cifar_brevitas_finetuning/cifar_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"292cfbcfee9ee087"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}