{"id":"https://openalex.org/W4416159282","doi":"https://doi.org/10.48550/arxiv.2511.07365","title":"Private Sketches for Linear Regression","display_name":"Private Sketches for Linear Regression","publication_year":2025,"publication_date":"2025-11-10","ids":{"openalex":"https://openalex.org/W4416159282","doi":"https://doi.org/10.48550/arxiv.2511.07365"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.07365","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07365","pdf_url":"https://arxiv.org/pdf/2511.07365","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.07365","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Das, Shrutimoy","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Das, Shrutimoy","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120494422","display_name":"Debanuj Nayak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nayak, Debanuj","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061449196","display_name":"Anirban Dasgupta","orcid":"https://orcid.org/0000-0002-8494-3692"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dasgupta, Anirban","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8942000269889832,"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":0.8942000269889832,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.066600002348423,"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/T10237","display_name":"Cryptography and Data Security","score":0.004699999932199717,"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/sketch","display_name":"Sketch","score":0.784600019454956},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5706999897956848},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4814999997615814},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44279998540878296},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4106999933719635}],"concepts":[{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.784600019454956},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5293999910354614},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42170000076293945},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4106999933719635},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3790000081062317},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3547999858856201},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.33169999718666077},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.3127000033855438},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2831000089645386},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.25920000672340393}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.07365","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07365","pdf_url":"https://arxiv.org/pdf/2511.07365","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.07365","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.07365","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.07365","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07365","pdf_url":"https://arxiv.org/pdf/2511.07365","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4416159282.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Linear":[0],"regression":[1,34,79,90,140],"is":[2,82,92],"frequently":[3],"applied":[4],"in":[5],"a":[6,51,95],"variety":[7],"of":[8,11,22,43,54,64,98,102,138,152,156,167,188,205,212,221],"domains,":[9],"some":[10],"which":[12,67],"might":[13],"contain":[14],"sensitive":[15],"information.":[16,29],"This":[17,81],"necessitates":[18],"that":[19,180],"the":[20,44,55,65,78,85,89,99,104,112,124,135,150,157,165,181,193,196,210,219],"application":[21,211],"these":[23,206],"methods":[24,129],"does":[25],"not":[26],"reveal":[27],"private":[28,31,41,62,154,162,207],"Differentially":[30],"(DP)":[32],"linear":[33,139],"methods,":[35],"developed":[36,132],"for":[37,133,149,164,200,216],"this":[38,143,147],"purpose,":[39],"compute":[40,73,192],"estimates":[42],"solution.":[45],"These":[46],"techniques":[47],"typically":[48],"involve":[49],"computing":[50],"noisy":[52],"version":[53],"solution":[56,76,109],"vector.":[57],"Instead,":[58],"we":[59],"propose":[60],"releasing":[61,153],"sketches":[63,155,163,208],"datasets,":[66],"can":[68,114],"then":[69],"be":[70,116],"used":[71],"to":[72,77,118,123,185],"an":[74],"approximate":[75],"problem.":[80,126],"motivated":[83],"by":[84],"\\emph{sketch-and-solve}":[86],"paradigm,":[87],"where":[88],"problem":[91,106],"solved":[93],"on":[94,103,111,195],"smaller":[96],"sketch":[97,113],"dataset":[100],"instead":[101],"original":[105,125],"space.":[107],"The":[108,203],"obtained":[110],"also":[115],"shown":[117],"have":[119,130],"good":[120],"approximation":[121],"guarantees":[122],"Various":[127],"sketching":[128],"been":[131],"improving":[134],"computational":[136],"efficiency":[137],"problems":[141,166],"under":[142],"paradigm.":[144],"We":[145,159,178,191],"adopt":[146],"paradigm":[148],"purpose":[151],"data.":[158],"construct":[160],"differentially":[161],"least":[168,174],"squares":[169],"regression,":[170,217],"as":[171,173],"well":[172],"absolute":[175],"deviations":[176],"regression.":[177,190],"show":[179],"privacy":[182,222],"constraints":[183],"lead":[184],"sketched":[186],"versions":[187],"regularized":[189],"bounds":[194],"regularization":[197],"parameter":[198],"required":[199],"guaranteeing":[201],"privacy.":[202],"availability":[204],"facilitates":[209],"commonly":[213],"available":[214],"solvers":[215],"without":[218],"risk":[220],"leakage.":[223]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-12T00:00:00"}
