{"id":"https://openalex.org/W3138774967","doi":"https://doi.org/10.1109/bigdata50022.2020.9378186","title":"Privacy Preserving Time-Series Forecasting of User Health Data Streams","display_name":"Privacy Preserving Time-Series Forecasting of User Health Data Streams","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3138774967","doi":"https://doi.org/10.1109/bigdata50022.2020.9378186","mag":"3138774967"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/2078.1/254003","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024134320","display_name":"Sana Imtiaz","orcid":"https://orcid.org/0000-0002-4088-8070"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Sana Imtiaz","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082803556","display_name":"Sonia-Florina Horchidan","orcid":null},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Sonia-Florina Horchidan","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101600034","display_name":"Zainab Abbas","orcid":"https://orcid.org/0000-0001-5203-5676"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zainab Abbas","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014557065","display_name":"Muhammad Arsalan","orcid":"https://orcid.org/0000-0002-6632-7300"},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke University Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Muhammad Arsalan","raw_affiliation_strings":["Otto-von-Guericke Universit\u00e4t Magdeburg, Magdeburg, Germany"],"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke Universit\u00e4t Magdeburg, Magdeburg, Germany","institution_ids":["https://openalex.org/I95793202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066122645","display_name":"Hassan Nazeer Chaudhry","orcid":"https://orcid.org/0000-0002-2307-0920"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Hassan Nazeer Chaudhry","raw_affiliation_strings":["DEIB, Politecnico di Milano, Milan, Italy"],"affiliations":[{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042422836","display_name":"Vladimir Vlassov","orcid":"https://orcid.org/0000-0002-6779-7435"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Vladimir Vlassov","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5024134320"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":1.3256,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85322726,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3428","last_page":"3437"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.954800009727478,"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/computer-science","display_name":"Computer science","score":0.8601970672607422},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6919549107551575},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6323220729827881},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6279743909835815},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5778120160102844},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.5761793851852417},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5352460145950317},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5123239755630493},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5109204649925232},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5065966844558716},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.500540018081665},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.45023781061172485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4143100082874298},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.22554785013198853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8601970672607422},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6919549107551575},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6323220729827881},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6279743909835815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5778120160102844},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.5761793851852417},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5352460145950317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5123239755630493},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5109204649925232},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5065966844558716},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.500540018081665},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.45023781061172485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4143100082874298},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.22554785013198853},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:dial.uclouvain.be:boreal:254003","is_oa":true,"landing_page_url":"http://hdl.handle.net/2078.1/254003","pdf_url":null,"source":{"id":"https://openalex.org/S4306401902","display_name":"Digital Access to Libraries (Universit\u00e9 catholique de Louvain (UCL), l'Universit\u00e9 de Namur (UNamur) and the Universit\u00e9 Saint-Louis (USL-B))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I95674353","host_organization_name":"UCLouvain","host_organization_lineage":["https://openalex.org/I95674353"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:dial.uclouvain.be:boreal:254003","is_oa":true,"landing_page_url":"http://hdl.handle.net/2078.1/254003","pdf_url":null,"source":{"id":"https://openalex.org/S4306401902","display_name":"Digital Access to Libraries (Universit\u00e9 catholique de Louvain (UCL), l'Universit\u00e9 de Namur (UNamur) and the Universit\u00e9 Saint-Louis (USL-B))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I95674353","host_organization_name":"UCLouvain","host_organization_lineage":["https://openalex.org/I95674353"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W151863654","https://openalex.org/W1557833142","https://openalex.org/W1870625491","https://openalex.org/W1873763122","https://openalex.org/W1981029888","https://openalex.org/W2027595342","https://openalex.org/W2037286920","https://openalex.org/W2053637704","https://openalex.org/W2064675550","https://openalex.org/W2097747115","https://openalex.org/W2099471712","https://openalex.org/W2138865266","https://openalex.org/W2149230623","https://openalex.org/W2167372639","https://openalex.org/W2283463896","https://openalex.org/W2541884796","https://openalex.org/W2566979091","https://openalex.org/W2621140322","https://openalex.org/W2777914285","https://openalex.org/W2899516827","https://openalex.org/W2905148628","https://openalex.org/W2908109788","https://openalex.org/W2911160483","https://openalex.org/W2913570153","https://openalex.org/W2950851762","https://openalex.org/W2953584774","https://openalex.org/W2963699739","https://openalex.org/W2965977697","https://openalex.org/W2970408908","https://openalex.org/W2970606380","https://openalex.org/W2977072935","https://openalex.org/W2978648093","https://openalex.org/W2981856777","https://openalex.org/W2995421442","https://openalex.org/W3006711551","https://openalex.org/W3013124972","https://openalex.org/W3038028469","https://openalex.org/W3080934299","https://openalex.org/W3102407811","https://openalex.org/W3159605734","https://openalex.org/W4289117554","https://openalex.org/W4298221930","https://openalex.org/W4318619660","https://openalex.org/W4320013936","https://openalex.org/W6606194391","https://openalex.org/W6639056083","https://openalex.org/W6657138077","https://openalex.org/W6663928093","https://openalex.org/W6695838908","https://openalex.org/W6728757088","https://openalex.org/W6731596640","https://openalex.org/W6746720608","https://openalex.org/W6757288671","https://openalex.org/W6757641797","https://openalex.org/W6759226220","https://openalex.org/W6764838729","https://openalex.org/W6768297860","https://openalex.org/W6768844495","https://openalex.org/W6772085293"],"related_works":["https://openalex.org/W3038283795","https://openalex.org/W2604501336","https://openalex.org/W2734500670","https://openalex.org/W2558166297","https://openalex.org/W2315671126","https://openalex.org/W798507144","https://openalex.org/W2964481303","https://openalex.org/W1751413323","https://openalex.org/W2571704763","https://openalex.org/W3133955889"],"abstract_inverted_index":{"Privacy":[0],"preservation":[1,15],"plays":[2],"a":[3,96,112,145,198,222,237,273],"vital":[4],"role":[5],"in":[6,19,26,136,201,210,214,242,260],"health":[7,33,140],"care":[8],"applications":[9],"as":[10,159,161,218,265,267,269],"the":[11,23,27,48,63,68,76,105,133,137,150,156,164,170,183,189,202,215,226,243,247,256],"requirements":[12],"for":[13,99,132],"privacy":[14,93,100,115,231],"are":[16,42,176],"very":[17],"strict":[18],"this":[20,119],"domain.":[21],"With":[22],"rapid":[24],"increase":[25],"amount,":[28],"quality":[29],"and":[30,53,116,123,130,172],"detail":[31],"of":[32,50,62,70,104,139,191,239,246],"data":[34,141],"being":[35],"gathered":[36],"with":[37,47,186,205],"smart":[38],"devices,":[39],"new":[40],"mechanisms":[41],"required":[43],"that":[44,66,84],"can":[45],"cope":[46],"challenges":[49],"large":[51],"scale":[52],"real-time":[54],"processing":[55],"requirements.":[56],"Federated":[57],"learning":[58],"(FL)":[59],"is":[60,95],"one":[61],"conventional":[64],"approaches":[65],"facilitate":[67],"training":[69,166,203,221],"AI":[71],"models":[72],"without":[73],"access":[74],"to":[75,110,148,154,188,207,220,271],"raw":[77],"data.":[78],"However,":[79],"recent":[80],"studies":[81],"have":[82],"shown":[83],"FL":[85,131],"alone":[86],"does":[87],"not":[88],"guarantee":[89],"sufficient":[90],"privacy.":[91],"Differential":[92],"(DP)":[94],"well-known":[97],"approach":[98],"guarantees,":[101],"however,":[102],"because":[103],"noise":[106],"addition,":[107],"DP":[108,129],"needs":[109],"make":[111],"trade-off":[113],"between":[114,152],"accuracy.":[117],"In":[118],"work,":[120],"we":[121],"design":[122],"implement":[124],"an":[125],"end-to-end":[126],"pipeline":[127],"using":[128,272],"first":[134],"time":[135],"context":[138],"streams.":[142],"We":[143],"propose":[144],"clustering":[146,195,253],"mechanism":[147,196,233,254],"leverage":[149],"similarities":[151],"users":[153],"improve":[155],"prediction":[157,211,244,257],"accuracy":[158,212,245],"well":[160],"significantly":[162],"reduce":[163],"model":[165,224],"time.":[167],"Depending":[168],"on":[169,225],"dataset":[171],"features,":[173],"our":[174,194,251],"predictions":[175],"no":[177],"more":[178],"than":[179],"0.025%":[180],"far":[181],"off":[182],"ground-truth":[184],"value":[185],"respect":[187],"range":[190],"value.":[192],"Moreover,":[193],"brings":[197],"significant":[199],"reduction":[200,209],"time,":[204],"up":[206],"49%":[208],"error":[213,258],"best":[216,235],"case,":[217],"compared":[219,270],"single":[223,274],"entire":[227],"dataset.":[228],"Our":[229],"proposed":[230,252],"preserving":[232],"at":[234],"introduces":[236],"decrease":[238],"\u2248":[240],"2%":[241],"trained":[248],"models.":[249],"Furthermore,":[250],"reduces":[255],"even":[259],"highly":[261],"noisy":[262],"settings":[263],"by":[264],"much":[266],"38%":[268],"federated":[275],"private":[276],"model.":[277]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
