{"id":"https://openalex.org/W7155392091","doi":"https://doi.org/10.48550/arxiv.2604.20274","title":"Estimating Power-Law Exponent with Edge Differential Privacy","display_name":"Estimating Power-Law Exponent with Edge Differential Privacy","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155392091","doi":"https://doi.org/10.48550/arxiv.2604.20274"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20274","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20274","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2604.20274","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134384355","display_name":"Adam Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Adam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134440628","display_name":"Mohamed Hefny","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hefny, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054794473","display_name":"Keval Vora","orcid":"https://orcid.org/0000-0002-5462-5116"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vora, Keval","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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9032999873161316,"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":0.9032999873161316,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.03999999910593033,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.022099999710917473,"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":0.8062000274658203},{"id":"https://openalex.org/keywords/exponent","display_name":"Exponent","score":0.6675000190734863},{"id":"https://openalex.org/keywords/degree-distribution","display_name":"Degree distribution","score":0.5515000224113464},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5282999873161316},{"id":"https://openalex.org/keywords/degree","display_name":"Degree (music)","score":0.5259000062942505},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5037999749183655},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4156999886035919},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.40230000019073486}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8062000274658203},{"id":"https://openalex.org/C2780388253","wikidata":"https://www.wikidata.org/wiki/Q5421508","display_name":"Exponent","level":2,"score":0.6675000190734863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6129000186920166},{"id":"https://openalex.org/C87414783","wikidata":"https://www.wikidata.org/wiki/Q1002603","display_name":"Degree distribution","level":3,"score":0.5515000224113464},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5282999873161316},{"id":"https://openalex.org/C2775997480","wikidata":"https://www.wikidata.org/wiki/Q586277","display_name":"Degree (music)","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3968000113964081},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3555000126361847},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3513000011444092},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3192000091075897},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30979999899864197},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2971000075340271},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20274","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20274","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20274","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20274","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.771270215511322,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many":[0],"real-world":[1],"graphs":[2,36],"have":[3],"degree":[4,67,125],"distributions":[5],"that":[6,24],"are":[7],"well":[8],"approximated":[9],"by":[10,93],"a":[11,20,65,72],"power-law,":[12],"and":[13,32,69,105,120,127,134,147],"the":[14,79,89,131,136],"corresponding":[15],"scaling":[16],"parameter":[17,111],"$\u03b1$":[18,41],"provides":[19],"compact":[21],"summary":[22],"of":[23,62],"structure":[25],"which":[26],"is":[27],"useful":[28],"for":[29,109,117],"graph":[30,141],"analysis":[31],"system":[33],"optimization.":[34],"When":[35],"contain":[37],"sensitive":[38],"relationship":[39],"data,":[40],"must":[42],"be":[43],"estimated":[44],"without":[45],"revealing":[46],"information":[47],"about":[48],"individual":[49],"edges.":[50],"This":[51],"paper":[52],"studies":[53],"power-law":[54,73],"exponent":[55],"estimation":[56],"under":[57],"edge":[58],"differential":[59],"privacy.":[60],"Instead":[61],"first":[63],"releasing":[64],"noisy":[66],"distribution":[68],"then":[70],"fitting":[71],"model,":[74],"we":[75,100],"propose":[76],"privatizing":[77],"only":[78],"low-dimensional":[80],"sufficient":[81],"statistics":[82],"needed":[83],"to":[84],"estimate":[85],"$\u03b1$,":[86],"thereby":[87],"avoiding":[88],"high":[90],"distortion":[91],"introduced":[92],"traditional":[94],"approaches.":[95],"Using":[96],"these":[97],"released":[98],"statistics,":[99],"support":[101],"both":[102,118],"discrete":[103],"approximation":[104],"likelihood-based":[106],"numerical":[107],"optimization":[108],"efficient":[110],"estimation.":[112],"We":[113],"develop":[114],"edge-DP":[115],"algorithms":[116],"centralized":[119],"local":[121,132],"DP":[122],"models,":[123],"compare":[124],"release":[126,129],"log-statistic":[128],"in":[130],"setting,":[133],"evaluate":[135],"resulting":[137],"methods":[138],"on":[139],"various":[140],"datasets":[142],"across":[143],"multiple":[144],"privacy":[145],"budgets":[146],"tail-cutoff":[148],"settings.":[149]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-24T00:00:00"}
