{"id":"https://openalex.org/W7160324707","doi":"https://doi.org/10.48550/arxiv.2605.02724","title":"Period-conscious Time-series Reconstruction under Local Differential Privacy","display_name":"Period-conscious Time-series Reconstruction under Local Differential Privacy","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160324707","doi":"https://doi.org/10.48550/arxiv.2605.02724"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02724","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02724","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135385665","display_name":"Yaxuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Yaxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135309231","display_name":"Tianxin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Tianxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135407534","display_name":"Enji Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Enji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135381466","display_name":"Yue Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135312555","display_name":"Yanran Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yanran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5135385665"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10920000076293945,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10920000076293945,"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/T11309","display_name":"Music and Audio Processing","score":0.08560000360012054,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11017","display_name":"Chaos-based Image/Signal Encryption","score":0.053300000727176666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6597999930381775},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.6502000093460083},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5346999764442444},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48910000920295715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46540001034736633},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.42890000343322754},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.38260000944137573},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.34929999709129333}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6597999930381775},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.6502000093460083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140999794006348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5360000133514404},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5063999891281128},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48910000920295715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46540001034736633},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3709000051021576},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.34929999709129333},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C2778465081","wikidata":"https://www.wikidata.org/wiki/Q5275356","display_name":"Differential phase","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2953999936580658},{"id":"https://openalex.org/C98644592","wikidata":"https://www.wikidata.org/wiki/Q184743","display_name":"Periodic function","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.274399995803833},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2619999945163727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02724","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":"doi:10.48550/arxiv.2605.02724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02724","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4684114456176758,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Periodic":[0],"patterns":[1],"are":[2,34],"fundamental":[3],"cues":[4],"in":[5,13,22,27,160],"multimedia":[6],"signals":[7],"and":[8,19,24,59,67,75,93,149],"systems,":[9],"including":[10],"repetitive":[11],"motion":[12],"video":[14],"(e.g.,":[15],"gait":[16],"cycles),":[17],"rhythmic":[18],"pitch-related":[20],"structure":[21,148],"audio,":[23],"recurring":[25],"textures":[26],"image":[28],"sequences.":[29],"When":[30],"such":[31],"user-generated":[32],"streams":[33],"collected":[35],"from":[36],"edge":[37],"devices,":[38],"local":[39],"differential":[40],"privacy":[41,134],"(LDP)":[42],"is":[43],"appealing":[44],"because":[45],"it":[46],"perturbs":[47],"data":[48],"before":[49],"upload;":[50],"however,":[51],"the":[52,118,161],"injected":[53],"noise":[54],"can":[55],"corrupt":[56],"spectral":[57,99],"peaks":[58],"induce":[60],"phase":[61,108,112],"drift,":[62],"making":[63],"period":[64,91],"estimation":[65],"unreliable":[66],"degrading":[68],"reconstruction":[69,80,153],"quality.":[70],"We":[71],"propose":[72],"\\textbf{CPR}":[73],"(\\textit{Cycle":[74],"Phase":[76],"Recovery}),":[77],"a":[78],"period-aware":[79],"framework":[81],"for":[82],"periodic":[83,140,147],"time":[84],"series":[85],"under":[86,132],"LDP.":[87],"CPR":[88,122,144],"performs":[89],"multi-scale":[90],"probing":[92],"multi-consensus":[94],"selection":[95],"to":[96,110],"suppress":[97],"noise-induced":[98],"interference,":[100],"then":[101],"aggregates":[102],"perturbed":[103],"samples":[104],"at":[105],"matched":[106],"within-cycle":[107],"positions":[109],"stabilize":[111],"alignment":[113],"across":[114],"cycles.":[115],"To":[116],"recover":[117],"underlying":[119],"per-phase":[120],"values,":[121],"combines":[123],"EM-based":[124],"denoising":[125],"with":[126],"kernel":[127],"density":[128],"estimation,":[129],"improving":[130],"robustness":[131],"tight":[133],"budgets.":[135],"Experiments":[136],"on":[137],"two":[138],"real-world":[139],"datasets":[141],"demonstrate":[142],"that":[143],"better":[145],"preserves":[146],"consistently":[150],"achieves":[151],"lower":[152],"error":[154],"than":[155],"representative":[156],"LDP":[157],"baselines,":[158],"especially":[159],"low-$\u03b5$":[162],"regime.":[163]},"counts_by_year":[],"updated_date":"2026-05-06T06:10:43.113611","created_date":"2026-05-06T00:00:00"}
