{"id":"https://openalex.org/W4408352450","doi":"https://doi.org/10.1109/icassp49660.2025.10889113","title":"Unveiling the Pruning Risks on Privacy Vulnerabilities of Deep Neural Networks","display_name":"Unveiling the Pruning Risks on Privacy Vulnerabilities of Deep Neural Networks","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408352450","doi":"https://doi.org/10.1109/icassp49660.2025.10889113"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079464446","display_name":"Wenxin Kuang","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxin Kuang","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qizhuang Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qizhuang Liang","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089450416","display_name":"Peng Sun","orcid":"https://orcid.org/0000-0001-6221-8142"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Sun","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067982858","display_name":"Wei Fu","orcid":"https://orcid.org/0000-0001-9118-9563"},"institutions":[{"id":"https://openalex.org/I2800710378","display_name":"Naval University of Engineering","ror":"https://ror.org/056vyez31","country_code":"CN","type":"education","lineage":["https://openalex.org/I2800710378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Fu","raw_affiliation_strings":["Naval University of Engineering,Department of Information Security,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naval University of Engineering,Department of Information Security,Wuhan,China","institution_ids":["https://openalex.org/I2800710378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102813955","display_name":"Qiao Hu","orcid":"https://orcid.org/0000-0002-6483-1431"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiao Hu","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069741536","display_name":"Yupeng Hu","orcid":"https://orcid.org/0000-0002-7358-7426"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yupeng Hu","raw_affiliation_strings":["Hunan University,College of Computer Science and Electronic Engineering,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University,College of Computer Science and Electronic Engineering,Changsha,China","institution_ids":["https://openalex.org/I16609230"]}]}],"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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9750000238418579,"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":0.9750000238418579,"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/computer-science","display_name":"Computer science","score":0.7259784936904907},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6596118211746216},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.575016438961029},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5657104253768921},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5568392276763916},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.44532835483551025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3410792946815491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7259784936904907},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6596118211746216},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.575016438961029},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5657104253768921},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5568392276763916},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.44532835483551025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3410792946815491},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W2747680751","https://openalex.org/W2928560789","https://openalex.org/W2962770929","https://openalex.org/W2962851801","https://openalex.org/W3034513523","https://openalex.org/W3035616549","https://openalex.org/W3195964985","https://openalex.org/W3208785972","https://openalex.org/W3215469691","https://openalex.org/W4229820657","https://openalex.org/W4288057780","https://openalex.org/W4290948187","https://openalex.org/W4308410483","https://openalex.org/W4312307529","https://openalex.org/W4312312856","https://openalex.org/W4312950730","https://openalex.org/W4319300051","https://openalex.org/W4323241012","https://openalex.org/W4386076651","https://openalex.org/W6726275242","https://openalex.org/W6754642193","https://openalex.org/W6772027524","https://openalex.org/W6781574218","https://openalex.org/W6795861741","https://openalex.org/W6803903061","https://openalex.org/W6838334194","https://openalex.org/W6846074030"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W2395294869","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W1998810860","https://openalex.org/W4206442282","https://openalex.org/W2384505857","https://openalex.org/W2355171581","https://openalex.org/W2964217848"],"abstract_inverted_index":{"Large-scale":[0],"deep":[1,62],"neural":[2],"networks":[3],"(DNNs),":[4],"such":[5],"as":[6,48,181],"large":[7],"language":[8],"models,":[9],"have":[10],"gained":[11],"immense":[12],"popularity":[13],"due":[14,31],"to":[15,32,52,110,153],"their":[16,23,115],"outstanding":[17],"performance":[18],"across":[19],"various":[20,111],"tasks.":[21],"However,":[22],"application":[24,174],"in":[25,158],"resource-constrained":[26],"scenarios":[27],"faces":[28],"significant":[29],"challenges":[30],"the":[33,58,76,91,98,131,156,172,186],"high":[34],"computational":[35,59],"costs":[36],"and":[37,113,120,134,147,195,207],"memory":[38],"usage":[39],"of":[40,61,79,93,101,175],"these":[41,54,137],"models":[42,149],"during":[43],"inference.":[44],"Model":[45],"pruning":[46,80,95,108,132,162,176,193,201],"emerges":[47],"a":[49,182],"viable":[50],"technique":[51],"mitigate":[53],"limitations":[55],"by":[56,105],"reducing":[57],"complexity":[60],"models.":[63],"While":[64],"existing":[65],"research":[66],"primarily":[67],"focuses":[68],"on":[69,97,144],"maximizing":[70],"inference":[71],"efficiency":[72],"without":[73],"compromising":[74],"accuracy,":[75],"privacy":[77,99,116,138,159,188],"implications":[78],"techniques":[81,96],"remain":[82],"largely":[83],"unexplored.":[84],"In":[85],"this":[86],"work,":[87],"we":[88,141,168],"systematically":[89],"investigate":[90],"impact":[92],"popular":[94],"vulnerabilities":[100],"DNNs.":[102],"We":[103,127],"begin":[104],"applying":[106],"common":[107],"schemes":[109,194],"DNNs":[112],"evaluating":[114],"risks,":[117],"both":[118,145,205],"before":[119],"after":[121],"pruning,":[122],"using":[123],"model":[124],"inversion":[125],"attacks.":[126],"then":[128],"analyze":[129],"how":[130],"rate":[133],"granularity":[135],"affect":[136],"vulnerabilities.":[139],"Moreover,":[140],"conduct":[142],"experiments":[143],"original":[146],"pruned":[148],"equipped":[150],"with":[151,191],"defenses":[152],"confirm":[154],"that":[155,203],"increase":[157],"risks":[160,189],"following":[161],"is":[163],"not":[164],"merely":[165],"coincidental.":[166],"Finally,":[167],"offer":[169],"guidelines":[170],"for":[171,199],"careful":[173],"techniques.":[177],"Our":[178],"findings":[179],"serve":[180],"cautionary":[183],"note,":[184],"highlighting":[185],"inherent":[187],"associated":[190],"current":[192],"providing":[196],"valuable":[197],"insights":[198],"developing":[200],"methods":[202],"are":[204],"efficient":[206],"secure.":[208]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}