{"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/information-sifting-funnel-privacy-preserving","title":"How Breakable Is Privacy: Probing and Resisting Model Inversion Attacks in Collaborative Inference","arxiv_id":"2501.00824","date":"2025-01-01","proceeding":null,"authors":["Rongke Liu","Youwen Zhu","Dong Wang","Gaoning Pan","Xingyu He","Weizhi Meng"],"abstract":"Collaborative inference (CI) improves computational efficiency for edge devices by transmitting intermediate features to cloud models. However, this process inevitably exposes feature representations to model inversion attacks (MIAs), enabling unauthorized data reconstruction. Despite extensive research, there is no established criterion for assessing the difficulty of MIA implementation, leaving a fundamental question unanswered: \\textit{What factors truly and verifiably determine the attack's success in CI?} Moreover, existing defenses lack the theoretical foundation described above, making it challenging to regulate feature information effectively while ensuring privacy and minimizing computational overhead. These shortcomings introduce three key challenges: theoretical gap, methodological limitation, and practical constraint. To overcome these challenges, we propose the first theoretical criterion to assess MIA difficulty in CI, identifying mutual information, entropy, and effective information volume as key influencing factors. The validity of this criterion is demonstrated by using the mutual information neural estimator. Building on this insight, we propose SiftFunnel, a privacy-preserving framework to resist MIA while maintaining usability. Specifically, we incorporate linear and non-linear correlation constraints alongside label smoothing to suppress redundant information transmission, effectively balancing privacy and usability. To enhance deployability, the edge model adopts a funnel-shaped structure with attention mechanisms, strengthening privacy while reducing computational and storage burdens. Experiments show that, compared to state-of-the-art defense, SiftFunnel increases reconstruction error by $\\sim$30\\%, lowers mutual and effective information metrics by $\\geq$50\\%, and reduces edge burdens by almost $20\\times$, while maintaining comparable usability.","url_abs":"https://arxiv.org/abs/2501.00824v7","url_pdf":"https://arxiv.org/pdf/2501.00824v7.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"information-sifting-funnel-privacy-preserving","repo_url":"https://github.com/SiftFunnel/SiftFunnel","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2501.00824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.00824"}},"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/SiftFunnel/SiftFunnel","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"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":0,"samples":[{"code_sha256_prefix":"4442ab29464bbc0e","entry":"get_pth_paths","repo":"SiftFunnel/SiftFunnel","repo_kind":"official","path":"train_inversion.py","file_url":"https://github.com/SiftFunnel/SiftFunnel/blob/HEAD/train_inversion.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4442ab29464bbc0e"}},{"code_sha256_prefix":"902096bb1a33d741","entry":"cosine_similarity","repo":"SiftFunnel/SiftFunnel","repo_kind":"official","path":"train_inversion.py","file_url":"https://github.com/SiftFunnel/SiftFunnel/blob/HEAD/train_inversion.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"902096bb1a33d741"}},{"code_sha256_prefix":"8c95fdfb2c19483c","entry":"mine_loss","repo":"SiftFunnel/SiftFunnel","repo_kind":"official","path":"Mutual_Information_estimator.py","file_url":"https://github.com/SiftFunnel/SiftFunnel/blob/HEAD/Mutual_Information_estimator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8c95fdfb2c19483c"}},{"code_sha256_prefix":"133fad3eb5762282","entry":"psnr","repo":"SiftFunnel/SiftFunnel","repo_kind":"official","path":"train_inversion.py","file_url":"https://github.com/SiftFunnel/SiftFunnel/blob/HEAD/train_inversion.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"133fad3eb5762282"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}