{"id":"https://openalex.org/W7162425666","doi":"https://doi.org/10.48550/arxiv.2605.24420","title":"Batch Normalization Amplifies Memorization and Privacy Risks","display_name":"Batch Normalization Amplifies Memorization and Privacy Risks","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162425666","doi":"https://doi.org/10.48550/arxiv.2605.24420"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24420","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24420","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.24420","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038942051","display_name":"Ngoc Phu Doan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doan, Ngoc Phu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137051375","display_name":"Chongyan Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Chongyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002123043","display_name":"Ihsen Alouani","orcid":"https://orcid.org/0000-0001-5102-8087"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alouani, Ihsen","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":0.8849999904632568,"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":0.8849999904632568,"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":0.054099999368190765,"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/T11424","display_name":"Security and Verification in Computing","score":0.009999999776482582,"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/memorization","display_name":"Memorization","score":0.9108999967575073},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7857000231742859},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5511999726295471},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.508400022983551},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.36419999599456787},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.35589998960494995}],"concepts":[{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.9108999967575073},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7857000231742859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503000259399414},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5138000249862671},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4287000000476837},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.35589998960494995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3296000063419342},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24420","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24420","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.24420","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24420","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7548465132713318}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Batch":[0],"Normalization":[1],"(BN)":[2],"is":[3],"widely":[4],"adopted":[5],"to":[6,80,102,122],"enable":[7],"faster":[8],"convergence":[9],"and":[10,23,47,77,88,161,165],"more":[11],"stable":[12],"training":[13,68,180],"of":[14,36,42,66,99,140,176],"deep":[15],"neural":[16],"networks.":[17],"However,":[18],"its":[19,48],"impact":[20],"on":[21,39],"privacy":[22,51,113,156],"memorization":[24,41,65,98,109],"has":[25],"remained":[26],"largely":[27],"unexplored.":[28],"In":[29],"this":[30,107,149],"work,":[31],"we":[32,90],"investigate":[33],"the":[34,40,97,137,174],"effect":[35],"BN":[37,94,117,135,160],"layers":[38,171],"atypical":[43],"or":[44,178],"outlier":[45,141],"samples":[46,142],"implications":[49],"for":[50],"leakage.":[52],"We":[53,124],"conduct":[54],"an":[55,154],"extensive":[56],"empirical":[57,127],"study":[58],"using":[59],"three":[60],"complementary":[61],"approaches:":[62],"(i)":[63],"unintended":[64],"out-of-distribution":[67],"samples,":[69],"(ii)":[70],"per-sample":[71],"influence":[72,139,175],"measured":[73],"via":[74],"gradient":[75],"norms,":[76],"(iii)":[78],"susceptibility":[79,121],"membership":[81],"inference":[82],"attacks":[83],"(MIA).":[84],"Across":[85],"multiple":[86],"datasets":[87],"architectures,":[89],"consistently":[91],"observe":[92],"that":[93,134],"substantially":[95],"increases":[96],"outliers":[100],"compared":[101],"models":[103,115],"without":[104],"BN.":[105],"Critically,":[106],"amplified":[108],"translates":[110],"directly":[111],"into":[112,148,168],"vulnerabilities:":[114],"with":[116,129,159],"exhibit":[118],"significantly":[119],"higher":[120],"MIAs.":[123],"complement":[125],"our":[126],"findings":[128],"a":[130],"theoretical":[131,166],"analysis":[132],"showing":[133],"amplifies":[136],"per-step":[138],"during":[143],"training,":[144],"providing":[145],"mechanistic":[146],"insight":[147],"phenomenon.":[150],"Our":[151],"results":[152],"highlight":[153],"underappreciated":[155],"risk":[157],"associated":[158],"provide":[162],"both":[163],"practical":[164],"insights":[167],"how":[169],"normalization":[170],"can":[172],"amplify":[173],"rare":[177],"sensitive":[179],"examples.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
