{"id":"https://openalex.org/W4416549289","doi":"https://doi.org/10.1145/3719027.3765045","title":"DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis","display_name":"DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W4416549289","doi":"https://doi.org/10.1145/3719027.3765045"},"language":null,"primary_location":{"id":"doi:10.1145/3719027.3765045","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765045","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765045","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765045","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045305534","display_name":"Chen Gong","orcid":"https://orcid.org/0000-0001-6178-4118"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Gong","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0001-6178-4118","affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023099893","display_name":"Kecen Li","orcid":"https://orcid.org/0009-0004-2266-8113"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kecen Li","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0009-0004-2266-8113","affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101422411","display_name":"Zinan Lin","orcid":"https://orcid.org/0000-0002-8421-2662"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zinan Lin","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8421-2662","affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100610986","display_name":"Tianhao Wang","orcid":"https://orcid.org/0000-0002-9017-7947"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianhao Wang","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-9017-7947","affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4139","last_page":"4153"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.16449999809265137,"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":0.16449999809265137,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.14429999887943268,"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":0.12099999934434891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6353999972343445},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.6348000168800354},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6123999953269958},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5767999887466431},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5310999751091003},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5246000289916992},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5138999819755554},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.366100013256073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7002999782562256},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6353999972343445},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.6348000168800354},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6123999953269958},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5767999887466431},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5310999751091003},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5138999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48579999804496765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43529999256134033},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4020000100135803},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.366100013256073},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C2989087649","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Image synthesis","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3719027.3765045","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765045","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765045","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3719027.3765045","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765045","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765045","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1335732772","display_name":"CICI:TCR: Enhancing Security and Privacy of Community Cyberinfrastructures for Collaborative Research","funder_award_id":"2319988","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5404517097","display_name":"Collaborative Research: IMR: MM-1B: Foundations for Differentially Private Internet Measurement","funder_award_id":"2220433","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416549289.pdf"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W1895577753","https://openalex.org/W1946137962","https://openalex.org/W1986280275","https://openalex.org/W2096870293","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2183341477","https://openalex.org/W2473418344","https://openalex.org/W2549139847","https://openalex.org/W2889232360","https://openalex.org/W2964194231","https://openalex.org/W3088157974","https://openalex.org/W3154109599","https://openalex.org/W4205228770","https://openalex.org/W4290991003","https://openalex.org/W4312283552","https://openalex.org/W4312933868","https://openalex.org/W4385187849","https://openalex.org/W4385269969","https://openalex.org/W4385571534","https://openalex.org/W4388857374","https://openalex.org/W4403981159"],"related_works":[],"abstract_inverted_index":{"Differentially":[0],"private":[1],"(DP)":[2],"image":[3,17,186],"synthesis":[4],"aims":[5],"to":[6,104,178],"generate":[7],"artificial":[8],"images":[9,25,201,209],"that":[10,34,112,158,182,193],"retain":[11],"the":[12,21,27,48,59,63,113,122,136,145,179,194,204,207],"properties":[13],"of":[14,23,50,116,148,206],"a":[15,155,165],"sensitive":[16,126,200],"dataset":[18],"while":[19],"protecting":[20],"privacy":[22,93],"individual":[24],"within":[26,164],"dataset.":[28],"Despite":[29,144],"recent":[30],"advancements,":[31],"we":[32,110,170,191],"find":[33,111,192],"inconsistent--and":[35],"sometimes":[36],"flawed--evaluation":[37],"protocols":[38],"have":[39,171],"been":[40],"applied":[41],"across":[42,72],"studies.":[43],"This":[44],"not":[45,129,212],"only":[46,130],"impedes":[47],"understanding":[49],"current":[51,160],"methods":[52,81,149],"but":[53,133],"also":[54,134],"hinders":[55],"future":[56,162],"advancements":[57],"in":[58],"field.":[60],"To":[61],"address":[62],"issue,":[64],"this":[65],"paper":[66],"introduces":[67],"DPImageBench,":[68,169],"with":[69],"thoughtful":[70],"design":[71],"several":[73,172],"dimensions:":[74],"(1)":[75],"Methods.":[76],"We":[77,97],"study":[78],"twelve":[79],"prominent":[80],"and":[82,92,101,150,161,199,210],"systematically":[83],"characterize":[84],"each":[85],"based":[86,120],"on":[87,121,125,184],"model":[88],"architecture,":[89],"pretraining":[90,183,198],"strategy,":[91],"mechanism.":[94],"(2)":[95],"Evaluation.":[96],"include":[98],"nine":[99],"datasets":[100,187],"seven":[102],"metrics":[103],"thoroughly":[105],"assess":[106],"these":[107],"methods.":[108],"Notably,":[109],"common":[114,180],"practice":[115],"selecting":[117],"downstream":[118],"classifiers":[119],"highest":[123],"accuracy":[124],"test":[127],"sets":[128],"violates":[131],"DP":[132],"overestimates":[135],"utility.":[137],"DPImageBench":[138,153],"corrects":[139],"for":[140],"it.":[141],"(3)":[142],"Platform.":[143],"wide":[146],"variety":[147],"evaluation":[151],"protocols,":[152],"provides":[154],"standardized":[156],"interface":[157],"accommodates":[159],"implementations":[163],"unified":[166],"framework.":[167],"With":[168],"noteworthy":[173],"findings.":[174],"For":[175],"example,":[176],"contrary":[177],"wisdom":[181],"public":[185],"is":[188,219],"usually":[189],"beneficial,":[190],"distributional":[195],"similarity":[196],"between":[197],"significantly":[202],"impacts":[203],"performance":[205],"synthetic":[208],"does":[211],"always":[213],"yield":[214],"improvements.":[215],"The":[216],"source":[217],"code":[218],"available.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-23T00:00:00"}