{"id":"https://openalex.org/W7164019451","doi":"https://doi.org/10.48550/arxiv.2606.08372","title":"SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)","display_name":"SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164019451","doi":"https://doi.org/10.48550/arxiv.2606.08372"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08372","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.08372","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092769442","display_name":"Steven Golob","orcid":"https://orcid.org/0009-0008-4442-2762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golob, Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138275786","display_name":"Sikha Pentyala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pentyala, Sikha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056749857","display_name":"Martine De Cock","orcid":"https://orcid.org/0000-0001-7917-0771"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Cock, Martine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.487199991941452,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.487199991941452,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.2912999987602234,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.04230000078678131,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.4480000138282776},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.43700000643730164},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4325000047683716},{"id":"https://openalex.org/keywords/memorization","display_name":"Memorization","score":0.4169999957084656},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.39570000767707825},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3921999931335449}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6298999786376953},{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.4480000138282776},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.414900004863739},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37549999356269836},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08372","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08372","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5626621246337891,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Synthetic":[0],"data":[1,88],"is":[2,112],"increasingly":[3],"promoted":[4],"as":[5],"a":[6,29,33,65,97,121,129,145,154],"privacy-preserving":[7],"substitute":[8],"for":[9,123],"releasing":[10],"sensitive":[11],"tabular":[12,61],"records,":[13,143],"yet":[14],"its":[15,193],"central":[16],"adversarial":[17],"threat":[18],"(\"reconstruction\",":[19],"the":[20,48,71,75,106,113,136,160,170,188,198,232],"recovery":[21],"of":[22,35,51,99,109,135,141,162,172,236],"an":[23],"individual's":[24],"hidden":[25],"attribute":[26,54],"values":[27],"from":[28,139],"synthetic":[30,60,87],"release":[31],"and":[32,59,96,144,150,201,218,238],"handful":[34],"known":[36],"quasi-identifiers)":[37],"has":[38],"been":[39],"studied":[40],"only":[41],"in":[42,105,231],"scattered,":[43],"hard-to-compare":[44],"settings.":[45],"We":[46,63],"present":[47],"first":[49],"systematization":[50],"reconstruction":[52,134,149,203],"(equivalently,":[53],"inference)":[55],"attacks":[56,69,84,101,217],"on":[57,153,213],"de-identified":[58],"data.":[62],"contribute":[64],"taxonomy":[66],"that":[67,102,132,147],"organizes":[68],"by":[70,187,223],"structure":[72,206],"they":[73],"exploit;":[74],"most":[76,199,202],"systematic":[77],"empirical":[78],"evaluation":[79],"to":[80],"date,":[81],"pitting":[82],"fourteen":[83],"against":[85],"nine":[86],"generation":[89],"(SDG)":[90],"methods":[91,196],"across":[92],"five":[93],"benchmark":[94],"datasets;":[95],"set":[98],"new":[100],"fill":[103],"gaps":[104],"taxonomy,":[107],"one":[108],"which":[110,183],"(CoBP-RA)":[111],"strongest":[114],"attack":[115,126],"we":[116,119],"measure.":[117],"Crucially,":[118],"introduce":[120],"methodology":[122],"interpreting":[124],"what":[125],"success":[127],"means:":[128],"memorization":[130,140],"test":[131],"distinguishes":[133],"population":[137],"distribution":[138],"training":[142],"reduction":[146],"places":[148],"membership":[151],"inference":[152],"single":[155],"comparable":[156],"scale.":[157],"Our":[158],"findings:":[159],"choice":[161,171],"SDG":[163],"method":[164],"governs":[165],"risk":[166,212],"far":[167],"more":[168],"than":[169,192,208],"attack;":[173],"differential":[174],"privacy":[175],"protects":[176],"mainly":[177],"at":[178],"small":[179],"budgets":[180],"($\\varepsilon\\lesssim1$),":[181],"above":[182],"protection":[184],"plateaus,":[185],"bounded":[186],"synthesizer's":[189],"capacity":[190],"rather":[191,207],"noise;":[194],"de-identification":[195],"are":[197,220],"exposed;":[200],"reflects":[204],"distributional":[205],"memorization,":[209],"concentrating":[210],"individual":[211],"atypical":[214],"records.":[215],"The":[216],"infrastructure":[219],"externally":[221],"validated":[222],"our":[224],"first-place":[225],"finish":[226],"among":[227],"all":[228],"red":[229],"teams":[230],"2025":[233],"\\textit{National":[234],"Institute":[235],"Standards":[237],"Technology}":[239],"(NIST)":[240],"Collaborative":[241],"Research":[242],"Cycle.":[243]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}