{"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/gs-wgan-a-gradient-sanitized-approach-for","title":"GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators","arxiv_id":"2006.08265","date":"2020-06-15","proceeding":"NeurIPS 2020 12","authors":["Dingfan Chen","Tribhuvanesh Orekondy","Mario Fritz"],"abstract":"The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e., decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets.","url_abs":"https://arxiv.org/abs/2006.08265v2","url_pdf":"https://arxiv.org/pdf/2006.08265v2.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":"gs-wgan-a-gradient-sanitized-approach-for","repo_url":"https://github.com/DingfanChen/GS-WGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2006.08265","atlas_url":"https://app.syntology.ai/?focus=2006.08265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08265"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/DingfanChen/GS-WGAN","reach":null}],"summary":{"ran":3,"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"listed":{"samples":6,"ran":5,"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":"53f434fe0e4156d9","entry":"GBlock","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"53f434fe0e4156d9"}},{"code_sha256_prefix":"355560a376ffab86","entry":"GeneratorResNet","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"355560a376ffab86"}},{"code_sha256_prefix":"ce077451cdef92e9","entry":"SpectralNorm","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ce077451cdef92e9"}},{"code_sha256_prefix":"a84f15a80e3dbb2a","entry":"l2_norm","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a84f15a80e3dbb2a"}},{"code_sha256_prefix":"8806db0f5a8e90fe","entry":"pixel_norm","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8806db0f5a8e90fe"}},{"code_sha256_prefix":"9bf347796ee07467","entry":"one_hot_embedding","repo":"DingfanChen/GS-WGAN","repo_kind":"listed","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.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":"9bf347796ee07467"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}