{"id":"https://openalex.org/W2767613317","doi":"https://doi.org/10.1145/3132847.3132990","title":"Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition","display_name":"Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2767613317","doi":"https://doi.org/10.1145/3132847.3132990","mag":"2767613317"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3132990","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132990","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","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/A5052577882","display_name":"Lingjuan Lyu","orcid":"https://orcid.org/0000-0003-3170-4994"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Lingjuan Lyu","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060320196","display_name":"Xuanli He","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuanli He","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020675850","display_name":"Yee Wei Law","orcid":"https://orcid.org/0000-0002-5665-0980"},"institutions":[{"id":"https://openalex.org/I170239107","display_name":"University of South Australia","ror":"https://ror.org/01p93h210","country_code":"AU","type":"education","lineage":["https://openalex.org/I170239107"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yee Wei Law","raw_affiliation_strings":["University of South Australia, Adelaide, Australia"],"affiliations":[{"raw_affiliation_string":"University of South Australia, Adelaide, Australia","institution_ids":["https://openalex.org/I170239107"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080554686","display_name":"Marimuthu Palaniswami","orcid":"https://orcid.org/0000-0002-3635-4252"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Marimuthu Palaniswami","raw_affiliation_strings":["University of Melbourne, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052577882"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":3.1181,"has_fulltext":false,"cited_by_count":83,"citation_normalized_percentile":{"value":0.93503598,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1219","last_page":"1228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997000098228455,"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":0.9997000098228455,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8732842206954956},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6790918111801147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6390137076377869},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5506295561790466},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5184504389762878},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.45466747879981995},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4248567819595337},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.40368416905403137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8732842206954956},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6790918111801147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6390137076377869},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5506295561790466},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5184504389762878},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.45466747879981995},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4248567819595337},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.40368416905403137},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3132990","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132990","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W3082479","https://openalex.org/W50268958","https://openalex.org/W123295786","https://openalex.org/W134960717","https://openalex.org/W155850149","https://openalex.org/W1486770802","https://openalex.org/W1557833142","https://openalex.org/W1873763122","https://openalex.org/W2022079499","https://openalex.org/W2028772593","https://openalex.org/W2040228409","https://openalex.org/W2064088392","https://openalex.org/W2064675550","https://openalex.org/W2074968817","https://openalex.org/W2089497633","https://openalex.org/W2098759488","https://openalex.org/W2125130625","https://openalex.org/W2125563538","https://openalex.org/W2128906841","https://openalex.org/W2148295146","https://openalex.org/W2160553465","https://openalex.org/W2161470918","https://openalex.org/W2164384758","https://openalex.org/W2270470215","https://openalex.org/W2339758167","https://openalex.org/W2397857137","https://openalex.org/W2610930722","https://openalex.org/W2949300694"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3090300519","https://openalex.org/W2514492205","https://openalex.org/W4250401876","https://openalex.org/W2943851981","https://openalex.org/W2566526749","https://openalex.org/W3047461507"],"abstract_inverted_index":{"The":[0,116],"proliferation":[1],"of":[2,10,17,150],"wearable":[3,169],"devices":[4],"has":[5],"contributed":[6],"to":[7,20,25,68,94,105,124],"the":[8,15,18,37,63,90,148,173],"emergence":[9],"mobile":[11],"crowdsensing,":[12],"which":[13],"leverages":[14],"power":[16],"crowd":[19],"collect":[21],"and":[22,32,97,135,155,181,187,199],"report":[23],"data":[24,80,104],"a":[26,46,57,71,82,106,110,143,190],"third":[27,38],"party":[28,39],"for":[29],"large-scale":[30],"sensing":[31],"collaborative":[33,139,192],"learning.":[34],"However,":[35],"since":[36],"may":[40],"not":[41],"be":[42],"honest,":[43],"privacy":[44],"poses":[45],"major":[47],"concern.":[48],"In":[49],"this":[50,54],"paper,":[51],"we":[52,141],"address":[53],"concern":[55],"with":[56],"two-stage":[58],"privacy-preserving":[59,191],"scheme":[60,119],"called":[61,85],"RG-RP:":[62],"first":[64],"stage":[65,92],"is":[66,196],"designed":[67],"mitigate":[69],"maximum":[70],"posteriori":[72],"(MAP)":[73],"estimation":[74,126],"attacks":[75,127],"by":[76,101,168],"perturbing":[77],"each":[78],"participant's":[79],"through":[81],"nonlinear":[83],"function":[84],"repeated":[86],"Gompertz":[87],"(RG);":[88],"while":[89],"second":[91],"aims":[93],"maintain":[95],"accuracy":[96],"reduce":[98],"transmission":[99],"energy":[100],"projecting":[102],"high-dimensional":[103],"lower":[107],"dimension,":[108],"using":[109],"row-orthogonal":[111],"random":[112],"projection":[113],"(RP)":[114],"matrix.":[115],"proposed":[117,142,174],"RG-RP":[118],"delivers":[120],"better":[121],"recovery":[122],"resistance":[123],"MAP":[125],"than":[128],"most":[129],"state-of-the-art":[130],"techniques":[131],"on":[132,162],"both":[133,197],"synthetic":[134],"real-world":[136],"datasets.":[137],"For":[138],"learning,":[140],"novel":[144],"LSTM-CNN":[145,175,188],"model":[146,176],"combining":[147],"merits":[149],"Long":[151],"Short-Term":[152],"Memory":[153],"(LSTM)":[154],"Convolutional":[156],"Neural":[157],"Networks":[158],"(CNN).":[159],"Our":[160],"experiments":[161],"two":[163],"representative":[164],"movement":[165],"datasets":[166],"captured":[167],"sensors":[170],"demonstrate":[171],"that":[172,195],"outperforms":[177],"standalone":[178],"LSTM,":[179],"CNN":[180],"Deep":[182],"Belief":[183],"Network.":[184],"Together,":[185],"RG+RP":[186],"provide":[189],"learning":[193],"framework":[194],"accurate":[198],"privacy-preserving.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
