{"id":"https://openalex.org/W4415367965","doi":"https://doi.org/10.1109/isit63088.2025.11195491","title":"Personalized Heterogeneous Mean Estimation Under User-Level LDP","display_name":"Personalized Heterogeneous Mean Estimation Under User-Level LDP","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415367965","doi":"https://doi.org/10.1109/isit63088.2025.11195491"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","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/A5029262439","display_name":"Ruida Zhou","orcid":"https://orcid.org/0000-0002-8855-2031"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruida Zhou","raw_affiliation_strings":["University of California, Los Angeles,Department of Electrical Engineering,Los Angeles"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles,Department of Electrical Engineering,Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057824853","display_name":"Antonious M. Girgis","orcid":"https://orcid.org/0000-0001-9828-6534"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonious M. Girgis","raw_affiliation_strings":["Google DeepMind,Mountain View"],"affiliations":[{"raw_affiliation_string":"Google DeepMind,Mountain View","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083980887","display_name":"Suhas Diggavi","orcid":"https://orcid.org/0000-0001-7313-9861"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhas Diggavi","raw_affiliation_strings":["University of California, Los Angeles,Department of Electrical Engineering,Los Angeles"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles,Department of Electrical Engineering,Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029262439"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28702735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9732000231742859,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9732000231742859,"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9038000106811523,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9038000106811523},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.6743999719619751},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.48829999566078186},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4781000018119812},{"id":"https://openalex.org/keywords/randomized-response","display_name":"Randomized response","score":0.4611000120639801},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.4251999855041504},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4235999882221222},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.41769999265670776}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.9038000106811523},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.6743999719619751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6103000044822693},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C2776441110","wikidata":"https://www.wikidata.org/wiki/Q1436628","display_name":"Randomized response","level":3,"score":0.4611000120639801},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4171000123023987},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3476000130176544},{"id":"https://openalex.org/C2909318450","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Unbiased Estimation","level":3,"score":0.3278000056743622},{"id":"https://openalex.org/C2993060064","wikidata":"https://www.wikidata.org/wiki/Q49918","display_name":"Population mean","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C181243257","wikidata":"https://www.wikidata.org/wiki/Q1693522","display_name":"Sample mean and sample covariance","level":3,"score":0.3147999942302704},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.3025999963283539},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C39425265","wikidata":"https://www.wikidata.org/wiki/Q7098944","display_name":"Optimal estimation","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3337222969","display_name":null,"funder_award_id":"2139304,2146838","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":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2911978475","https://openalex.org/W2995022099","https://openalex.org/W3102984355","https://openalex.org/W3212195337","https://openalex.org/W4250954493","https://openalex.org/W4289655333"],"related_works":[],"abstract_inverted_index":{"We":[0,22],"study":[1],"personalized":[2],"heterogeneous":[3],"mean":[4,47,100],"estimation":[5,95,101],"under":[6],"user-level":[7,111],"local":[8,17,57,112],"differential":[9,113],"privacy":[10,15,54,114],"(LDP),":[11],"which":[12],"protects":[13],"the":[14,46,53,61,79,83,90,108],"of":[16,36,48,55,78,85],"datasets":[18],"with":[19,27,34,70],"multiple":[20],"samples.":[21],"consider":[23],"a":[24],"distributed":[25],"environment":[26],"<tex":[28,37],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[29,38],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$n$</tex>":[30],"users,":[31],"each":[32,49,103],"associated":[33],"one":[35],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$k$</tex>":[39],"clusters.":[40,86],"Our":[41,87],"goal":[42],"is":[43],"to":[44],"estimate":[45],"cluster":[50],"while":[51],"preserving":[52],"users'":[56],"datasets.":[58],"Focusing":[59],"on":[60,107],"scalar":[62],"case,":[63],"we":[64],"propose":[65],"algorithms":[66],"that":[67],"handle":[68],"scenarios":[69],"(unknown)":[71],"equal":[72],"and":[73,81,96,116],"unequal":[74],"variance":[75],"proxies":[76],"(spans":[77],"clusters),":[80],"even":[82],"number":[84],"methods":[88],"identify":[89],"clusters":[91],"via":[92],"private":[93,99],"frequency":[94],"subsequently":[97],"perform":[98],"for":[102],"cluster.":[104],"Theoretical":[105],"guarantees":[106,115],"trade-off":[109],"between":[110],"performance":[117],"are":[118],"provided.":[119]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-21T00:00:00"}
