{"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/iterative-methods-for-private-synthetic-data","title":"Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods","arxiv_id":"2106.07153","date":"2021-06-14","proceeding":"NeurIPS 2021 12","authors":["Terrance Liu","Giuseppe Vietri","Zhiwei Steven Wu"],"abstract":"We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries. We first present an algorithmic framework that unifies a long line of iterative algorithms in the literature. Under this framework, we propose two new methods. The first method, private entropy projection (PEP), can be viewed as an advanced variant of MWEM that adaptively reuses past query measurements to boost accuracy. Our second method, generative networks with the exponential mechanism (GEM), circumvents computational bottlenecks in algorithms such as MWEM and PEP by optimizing over generative models parameterized by neural networks, which capture a rich family of distributions while enabling fast gradient-based optimization. We demonstrate that PEP and GEM empirically outperform existing algorithms. Furthermore, we show that GEM nicely incorporates prior information from public data while overcoming limitations of PMW^Pub, the existing state-of-the-art method that also leverages public data.","url_abs":"https://arxiv.org/abs/2106.07153v2","url_pdf":"https://arxiv.org/pdf/2106.07153v2.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":"iterative-methods-for-private-synthetic-data","repo_url":"https://github.com/terranceliu/iterative-dp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.07153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07153"}},"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/terranceliu/pmw-pub","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/terranceliu/iterative-dp","reach":{"status":"ok"}}],"summary":{"ran":4,"ran_draft_wrong":1,"unverified":5},"by_repo_kind":{"found_in_text":{"samples":10,"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":10,"samples":[{"code_sha256_prefix":"a47f1575ae562b35","entry":"CliqueVector","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a47f1575ae562b35"}},{"code_sha256_prefix":"b61186690939de22","entry":"Domain","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b61186690939de22"}},{"code_sha256_prefix":"835c02f2746ec14e","entry":"TorchSparse","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"835c02f2746ec14e"}},{"code_sha256_prefix":"7bc3b8499b22a4f0","entry":"greedy_order","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7bc3b8499b22a4f0"}},{"code_sha256_prefix":"dd9e1796254d2510","entry":"variable_elimination","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"dd9e1796254d2510"}},{"code_sha256_prefix":"6c76176ca38b93e8","entry":"Dataset","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"6c76176ca38b93e8"}},{"code_sha256_prefix":"1dbdeebf247fdef6","entry":"Factor","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"1dbdeebf247fdef6"}},{"code_sha256_prefix":"7655c4ec1b8d6a56","entry":"FactoredInference","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"7655c4ec1b8d6a56"}},{"code_sha256_prefix":"f2ed97bad0d8a614","entry":"GraphicalModel","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"f2ed97bad0d8a614"}},{"code_sha256_prefix":"5308c885b0ee8779","entry":"JunctionTree","repo":"terranceliu/pmw-pub","repo_kind":"found_in_text","path":"mbi/inference.py","file_url":"https://github.com/terranceliu/pmw-pub/blob/HEAD/mbi/inference.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":"5308c885b0ee8779"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}