{"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/noise-aware-statistical-inference-with","title":"Noise-Aware Statistical Inference with Differentially Private Synthetic Data","arxiv_id":"2205.14485","date":"2022-05-28","proceeding":null,"authors":["Ossi Räisä","Joonas Jälkö","Samuel Kaski","Antti Honkela"],"abstract":"While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities. For example, confidence intervals become too narrow, which we demonstrate with a simple experiment. We tackle this problem by combining synthetic data analysis techniques from the field of multiple imputation (MI), and synthetic data generation using noise-aware (NA) Bayesian modeling into a pipeline NA+MI that allows computing accurate uncertainty estimates for population-level quantities from DP synthetic data. To implement NA+MI for discrete data generation using the values of marginal queries, we develop a novel noise-aware synthetic data generation algorithm NAPSU-MQ using the principle of maximum entropy. Our experiments demonstrate that the pipeline is able to produce accurate confidence intervals from DP synthetic data. The intervals become wider with tighter privacy to accurately capture the additional uncertainty stemming from DP noise.","url_abs":"https://arxiv.org/abs/2205.14485v3","url_pdf":"https://arxiv.org/pdf/2205.14485v3.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":"noise-aware-statistical-inference-with","repo_url":"https://github.com/dpbayes/napsu-mq-experiments","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"noise-aware-statistical-inference-with","repo_url":"https://github.com/DPBayes/twinify","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok"}}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.14485","atlas_url":"https://app.syntology.ai/?focus=2205.14485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14485"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dpbayes/napsu-mq-experiments","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/DPBayes/twinify","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":"94ba296c50c79156","entry":"all_marginals","repo":"dpbayes/napsu-mq-experiments","repo_kind":"official","path":"lib/marginal_query.py","file_url":"https://github.com/dpbayes/napsu-mq-experiments/blob/HEAD/lib/marginal_query.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":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"94ba296c50c79156"}},{"code_sha256_prefix":"f21b2ac817a00877","entry":"all_marginals_for_feature_set","repo":"dpbayes/napsu-mq-experiments","repo_kind":"official","path":"lib/marginal_query.py","file_url":"https://github.com/dpbayes/napsu-mq-experiments/blob/HEAD/lib/marginal_query.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":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f21b2ac817a00877"}},{"code_sha256_prefix":"7aa691eca99752cd","entry":"join_query_sets","repo":"dpbayes/napsu-mq-experiments","repo_kind":"official","path":"lib/marginal_query.py","file_url":"https://github.com/dpbayes/napsu-mq-experiments/blob/HEAD/lib/marginal_query.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":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7aa691eca99752cd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}