{"id":"https://openalex.org/W4416549593","doi":"https://doi.org/10.1145/3719027.3744839","title":"Mitigating Data Poisoning Attacks to Local Differential Privacy","display_name":"Mitigating Data Poisoning Attacks to Local Differential Privacy","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W4416549593","doi":"https://doi.org/10.1145/3719027.3744839"},"language":null,"primary_location":{"id":"doi:10.1145/3719027.3744839","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3744839","pdf_url":null,"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://doi.org/10.1145/3719027.3744839","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100354086","display_name":"Xiaolin Li","orcid":"https://orcid.org/0009-0003-5205-9610"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolin Li","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":"https://orcid.org/0009-0003-5205-9610","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101471208","display_name":"Ninghui Li","orcid":"https://orcid.org/0000-0001-8207-9717"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ninghui Li","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":"https://orcid.org/0000-0001-8207-9717","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725035","display_name":"Boyang Wang","orcid":"https://orcid.org/0000-0001-8973-2328"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boyang Wang","raw_affiliation_strings":["The University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":"https://orcid.org/0000-0001-8973-2328","affiliations":[{"raw_affiliation_string":"The University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045191954","display_name":"Wenhai Sun","orcid":"https://orcid.org/0000-0003-0458-0092"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhai Sun","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":"https://orcid.org/0000-0003-0458-0092","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"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":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1230","last_page":"1244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9377999901771545,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9377999901771545,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.023499999195337296,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.004800000227987766,"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/differential-privacy","display_name":"Differential privacy","score":0.7335000038146973},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.697700023651123},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5950000286102295},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.5626000165939331},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.41600000858306885},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.3497999906539917},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.32109999656677246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652999758720398},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.7335000038146973},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.697700023651123},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6782000064849854},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5950000286102295},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.41600000858306885},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3719027.3744839","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3744839","pdf_url":null,"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:10.1145/3719027.3744839","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3744839","pdf_url":null,"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/G2772634139","display_name":null,"funder_award_id":"CNS-2238680,CNS-2207204,CNS-2247794","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1981029888","https://openalex.org/W1986293063","https://openalex.org/W2005825070","https://openalex.org/W2063522056","https://openalex.org/W2532967691","https://openalex.org/W2930558539","https://openalex.org/W2948055046","https://openalex.org/W2963629772","https://openalex.org/W2963881987","https://openalex.org/W2964138696","https://openalex.org/W3006678129","https://openalex.org/W3032754641","https://openalex.org/W3126700118","https://openalex.org/W4400909957","https://openalex.org/W4408749872"],"related_works":[],"abstract_inverted_index":{"The":[0],"distributed":[1],"nature":[2],"of":[3,38,120,160],"local":[4],"differential":[5],"privacy":[6],"(LDP)":[7],"invites":[8],"data":[9,129,165],"poisoning":[10],"attacks":[11],"and":[12,48,79,96,133,144,167,184],"poses":[13],"unforeseen":[14],"threats":[15],"to":[16,54,74],"the":[17,87,91,118],"underlying":[18],"LDP-supported":[19],"applications.":[20],"In":[21,52,153],"this":[22],"paper,":[23],"we":[24,57,69,104,155,175],"propose":[25,106,168],"a":[26,36,71,107,169],"comprehensive":[27],"mitigation":[28,65],"framework":[29],"for":[30,63,163,188],"popular":[31],"frequency":[32],"estimation,":[33],"which":[34,174],"contains":[35],"suite":[37],"novel":[39],"defenses,":[40],"including":[41],"malicious":[42,100],"user":[43],"detection,":[44,68],"attack":[45,92,131],"pattern":[46],"recognition,":[47],"damaged":[49],"utility":[50],"recovery.":[51],"addition":[53],"existing":[55],"attacks,":[56],"explore":[58],"new":[59,72,170,177],"adaptive":[60],"adversarial":[61,114],"activities":[62],"our":[64],"design.":[66],"For":[67],"present":[70],"method":[73],"precisely":[75],"identify":[76],"bogus":[77],"reports,":[78],"thus":[80,116],"LDP":[81,161],"aggregation":[82],"can":[83,110],"be":[84],"performed":[85],"over":[86,147],"''clean''":[88],"data.":[89],"When":[90],"behavior":[93],"becomes":[94],"stealthy":[95],"direct":[97],"filtering":[98],"out":[99],"users":[101],"is":[102],"difficult,":[103],"further":[105],"detection":[108,124],"that":[109],"effectively":[111],"recognize":[112],"hidden":[113],"patterns,":[115],"facilitating":[117],"decision-making":[119],"service":[121],"providers.":[122],"These":[123],"methods":[125],"require":[126],"no":[127],"additional":[128],"or":[130],"information":[132],"incur":[134],"minimal":[135],"computational":[136],"cost.":[137],"Our":[138],"experiment":[139],"demonstrates":[140],"their":[141],"excellent":[142],"performance":[143],"substantial":[145],"improvement":[146],"previous":[148],"work":[149],"in":[150,182],"various":[151],"settings.":[152],"addition,":[154],"conduct":[156],"an":[157],"empirical":[158],"analysis":[159],"post-processing":[162,171],"corrupted":[164],"recovery":[166],"method,":[172],"through":[173],"reveal":[176],"insights":[178],"into":[179],"protocol":[180],"recommendations":[181],"practice":[183],"key":[185],"design":[186],"principles":[187],"future":[189],"research.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2025-11-23T00:00:00"}
