{"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":"/code/reconstruction-costs","entry":"reconstruction_costs","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":3,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2603.17623","paper":"/paper/arxiv-2603-17623","title":"ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"gaow0007/ATSPrivacy","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/gaow0007/ATSPrivacy/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0291b9e998a5906","mcp_get_code":{"code_sha256":"b0291b9e998a5906"}},{"arxiv_id":"2404.03233","paper":"/paper/learn-what-you-want-to-unlearn-unlearning","title":"Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning","date":null,"month_inferred_from_arxiv_id":"2024-04","title_source":"archive","repo":"tasi-lab/unlearning-inversion-attacks","path":"recovery/recovery_algo.py","file_url":"https://github.com/tasi-lab/unlearning-inversion-attacks/blob/HEAD/recovery/recovery_algo.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d696213f6c2f41c9","mcp_get_code":{"code_sha256":"d696213f6c2f41c9"}},{"arxiv_id":"2309.13016","paper":"/paper/understanding-deep-gradient-leakage-via-1","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/inversion-influence-function","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/illidanlab/inversion-influence-function/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d696213f6c2f41c9","mcp_get_code":{"code_sha256":"d696213f6c2f41c9"}},{"arxiv_id":"2309.13016","paper":"/paper/understanding-deep-gradient-leakage-via-1","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/inversion-influence-function","path":"myreconstruction.py","file_url":"https://github.com/illidanlab/inversion-influence-function/blob/HEAD/myreconstruction.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a3f6f7c1e8f15fe","mcp_get_code":{"code_sha256":"7a3f6f7c1e8f15fe"}},{"arxiv_id":"2308.04699","paper":"/paper/gifd-a-generative-gradient-inversion-method","title":"GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffhibnese/GIFD","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/ffhibnese/GIFD/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24dd50ff238a3c31","mcp_get_code":{"code_sha256":"24dd50ff238a3c31"}},{"arxiv_id":"2110.14962","paper":"/paper/gradient-inversion-with-generative-image","title":"Gradient Inversion with Generative Image Prior","date":"2021-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/gradient-inversion-generative-image-prior","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/ml-postech/gradient-inversion-generative-image-prior/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fa7161dab184c21","mcp_get_code":{"code_sha256":"2fa7161dab184c21"}},{"arxiv_id":"2107.01154","paper":"/paper/gradient-leakage-resilient-federated-learning","title":"Gradient-Leakage Resilient Federated Learning","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaiyuanZh/censor","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/KaiyuanZh/censor/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24dd50ff238a3c31","mcp_get_code":{"code_sha256":"24dd50ff238a3c31"}},{"arxiv_id":"2003.14053","paper":"/paper/inverting-gradients-how-easy-is-it-to-break","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JonasGeiping/invertinggradients","path":"inversefed/reconstruction_algorithms.py","file_url":"https://github.com/JonasGeiping/invertinggradients/blob/HEAD/inversefed/reconstruction_algorithms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d696213f6c2f41c9","mcp_get_code":{"code_sha256":"d696213f6c2f41c9"}},{"arxiv_id":"Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper","paper":null,"title":"arXiv:Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jeremy313/Soteria","path":"GS_attack/inversefed/reconstruction_algorithms.py","file_url":"https://github.com/jeremy313/Soteria/blob/HEAD/GS_attack/inversefed/reconstruction_algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a42040fd32ba058","mcp_get_code":{"code_sha256":"7a42040fd32ba058"}}]}