{"id":"https://openalex.org/W7138173269","doi":"https://doi.org/10.48550/arxiv.2603.13722","title":"TableMark: A Multi-bit Watermark for Synthetic Tabular Data","display_name":"TableMark: A Multi-bit Watermark for Synthetic Tabular Data","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138173269","doi":"https://doi.org/10.48550/arxiv.2603.13722"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13722","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":null,"license_id":null,"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.2603.13722","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129663462","display_name":"Yuyang Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Yuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129706143","display_name":"Yaoqiang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yaoqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129641952","display_name":"Chen Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129748007","display_name":"Yang Li","orcid":"https://orcid.org/0009-0003-0093-4284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428059","display_name":"Ran Li","orcid":"https://orcid.org/0000-0001-7475-759X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guoliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129657399","display_name":"Jianhua Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Jianhua","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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.23909999430179596,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.23909999430179596,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.21729999780654907,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.1809999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/digital-watermarking","display_name":"Digital watermarking","score":0.8608999848365784},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8165000081062317},{"id":"https://openalex.org/keywords/watermark","display_name":"Watermark","score":0.8051000237464905},{"id":"https://openalex.org/keywords/shuffling","display_name":"Shuffling","score":0.7003999948501587},{"id":"https://openalex.org/keywords/traceability","display_name":"Traceability","score":0.6596999764442444},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5400999784469604},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.5331000089645386},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4092999994754791}],"concepts":[{"id":"https://openalex.org/C150817343","wikidata":"https://www.wikidata.org/wiki/Q875932","display_name":"Digital watermarking","level":3,"score":0.8608999848365784},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8165000081062317},{"id":"https://openalex.org/C164112704","wikidata":"https://www.wikidata.org/wiki/Q7974348","display_name":"Watermark","level":3,"score":0.8051000237464905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7576000094413757},{"id":"https://openalex.org/C167927819","wikidata":"https://www.wikidata.org/wiki/Q1930567","display_name":"Shuffling","level":2,"score":0.7003999948501587},{"id":"https://openalex.org/C153876917","wikidata":"https://www.wikidata.org/wiki/Q899704","display_name":"Traceability","level":2,"score":0.6596999764442444},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47600001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C3073032","wikidata":"https://www.wikidata.org/wiki/Q15912075","display_name":"Information hiding","level":3,"score":0.3537999987602234},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3222000002861023},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C182548165","wikidata":"https://www.wikidata.org/wiki/Q2261483","display_name":"Scrambling","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C178489894","wikidata":"https://www.wikidata.org/wiki/Q8789","display_name":"Cryptography","level":2,"score":0.2619999945163727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13722","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13722","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Watermarking":[0],"has":[1],"emerged":[2],"as":[3,23,94],"an":[4],"effective":[5,113],"solution":[6],"for":[7,43],"copyright":[8],"protection":[9],"of":[10,47,141],"synthetic":[11,18,76],"data.":[12],"However,":[13],"applying":[14],"watermarking":[15,69,157],"techniques":[16],"to":[17,40,60,103,116,120,145],"tabular":[19,24,50,77,156],"data":[20,25,51,55,101,123],"presents":[21],"challenges,":[22],"can":[26],"easily":[27],"lose":[28],"their":[29],"watermarks":[30,74],"through":[31],"shuffling":[32],"or":[33],"deletion":[34],"operations.":[35],"The":[36],"major":[37],"challenge":[38],"is":[39,143],"provide":[41],"traceability":[42,81],"tracking":[44],"multiple":[45],"users":[46],"the":[48,90,100,122],"watermarked":[49],"while":[52,84],"maintaining":[53,85],"high":[54,86,149],"utility":[56],"and":[57,82,108,147],"robustness":[58,83,107],"(resistance":[59],"attacks).":[61],"To":[62],"address":[63],"this,":[64],"we":[65,111],"design":[66],"a":[67,95,138],"multi-bit":[68],"scheme":[70],"TableMark":[71,135,152],"that":[72,134],"encodes":[73],"into":[75],"data,":[78],"ensuring":[79],"superior":[80],"utility.":[87,109,124,150],"We":[88],"formulate":[89],"watermark":[91],"encoding":[92],"process":[93],"constrained":[96],"optimization":[97,114],"problem,":[98],"allowing":[99],"owner":[102],"effectively":[104,136],"trade":[105],"off":[106],"Additionally,":[110],"propose":[112],"mechanisms":[115],"solve":[117],"this":[118],"problem":[119],"enhance":[121],"Experimental":[125],"results":[126],"on":[127],"four":[128],"widely":[129],"used":[130],"real-world":[131],"datasets":[132],"show":[133],"traces":[137],"large":[139],"number":[140],"users,":[142],"resilient":[144],"attacks,":[146],"preserves":[148],"Moreover,":[151],"significantly":[153],"outperforms":[154],"state-of-the-art":[155],"schemes.":[158]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-18T00:00:00"}