{"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/towards-secure-and-practical-machine-learning","title":"Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation","arxiv_id":"2108.07463","date":"2021-08-17","proceeding":null,"authors":["Fei Zheng","Chaochao Chen","Xiaolin Zheng","Mingjie Zhu"],"abstract":"With the increasing demands for privacy protection, privacy-preserving machine learning has been drawing much attention in both academia and industry. However, most existing methods have their limitations in practical applications. On the one hand, although most cryptographic methods are provable secure, they bring heavy computation and communication. On the other hand, the security of many relatively efficient private methods (e.g., federated learning and split learning) is being questioned, since they are non-provable secure. Inspired by previous work on privacy-preserving machine learning, we build a privacy-preserving machine learning framework by combining random permutation and arithmetic secret sharing via our compute-after-permutation technique. Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods. Moreover, by adopting distance correlation as a metric for privacy leakage, we demonstrate that our method is more secure than previous non-provable secure methods. Overall, our proposal achieves a good balance between security and efficiency. Experimental results show that our method not only is up to 6x faster and reduces up to 85% network traffic compared with state-of-the-art cryptographic methods, but also leaks less privacy during the training process compared with non-provable secure methods.","url_abs":"https://arxiv.org/abs/2108.07463v3","url_pdf":"https://arxiv.org/pdf/2108.07463v3.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":"towards-secure-and-practical-machine-learning","repo_url":"https://github.com/zfscgy/Amber","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"},{"task_slug":"privacy-preserving-deep-learning","task_name":"Privacy Preserving Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.07463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07463"}},"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/zfscgy/Amber","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"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":1,"samples":[{"code_sha256_prefix":"b0275799c91f225e","entry":"check_op_input","repo":"zfscgy/Amber","repo_kind":"official","path":"Amber/NN/Operator.py","file_url":"https://github.com/zfscgy/Amber/blob/HEAD/Amber/NN/Operator.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b0275799c91f225e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}