{"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/design-encrypted-gnn-inference-via-server","title":"DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning","arxiv_id":"2507.05649","date":"2025-07-08","proceeding":null,"authors":["Kaixiang Zhao","Joseph Yousry Attalla","Qian Lou","Yushun Dong"],"abstract":"Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically incurs substantial computational overhead, rendering real-time and privacy-preserving inference impractical. In this work, we propose DESIGN (EncrypteD GNN Inference via sErver-Side Input Graph pruNing), a novel framework for efficient encrypted GNN inference. DESIGN tackles the critical efficiency limitations of existing FHE GNN approaches, which often overlook input data redundancy and apply uniform computational strategies. Our framework achieves significant performance gains through a hierarchical optimization strategy executed entirely on the server: first, FHE-compatible node importance scores (based on encrypted degree statistics) are computed from the encrypted graph. These scores then guide a homomorphic partitioning process, generating multi-level importance masks directly under FHE. This dynamically generated mask facilitates both input graph pruning (by logically removing unimportant elements) and a novel adaptive polynomial activation scheme, where activation complexity is tailored to node importance levels. Empirical evaluations demonstrate that DESIGN substantially accelerates FHE GNN inference compared to state-of-the-art methods while maintaining competitive model accuracy, presenting a robust solution for secure graph analytics.","url_abs":"https://arxiv.org/abs/2507.05649v1","url_pdf":"https://arxiv.org/pdf/2507.05649v1.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":[],"tasks":[{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2507.05649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.05649"}},"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":"deterministic:regex_extraction","url":"https://github.com/LabRAI/DESIGN","reach":{"status":"ok"}}],"summary":{"unverified":6},"by_repo_kind":{"found_in_text":{"samples":6,"ran":0,"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":6,"samples":[{"code_sha256_prefix":"92515459fb6ec3a4","entry":"decrypt_outputs","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"design_model.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/design_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"92515459fb6ec3a4"}},{"code_sha256_prefix":"35dabeb7396b6c8f","entry":"decrypt_outputs","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"openfhe_model.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/openfhe_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"35dabeb7396b6c8f"}},{"code_sha256_prefix":"461dd129f8dd85c3","entry":"decrypt_outputs","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"seal_model.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/seal_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"461dd129f8dd85c3"}},{"code_sha256_prefix":"0f623a77c75aa8ec","entry":"get_A_bar","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0f623a77c75aa8ec"}},{"code_sha256_prefix":"0e3020c50422519f","entry":"get_A_unnormalized","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0e3020c50422519f"}},{"code_sha256_prefix":"821efa2b8dd8038f","entry":"get_subgraph","repo":"LabRAI/DESIGN","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/LabRAI/DESIGN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"821efa2b8dd8038f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}