{"id":"https://openalex.org/W2798717155","doi":"https://doi.org/10.1109/acssc.2017.8335645","title":"Bayesian time series matching and privacy","display_name":"Bayesian time series matching and privacy","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2798717155","doi":"https://doi.org/10.1109/acssc.2017.8335645","mag":"2798717155"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2017.8335645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2017.8335645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","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/A5102020099","display_name":"Ke Li","orcid":"https://orcid.org/0000-0002-2236-6578"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ke Li","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Massachusetts, Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Massachusetts, Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009685038","display_name":"Hossein Pishro-Nik","orcid":"https://orcid.org/0000-0002-7249-2548"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hossein Pishro-Nik","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Massachusetts, Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Massachusetts, Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064436231","display_name":"Dennis Goeckel","orcid":"https://orcid.org/0000-0002-4190-9515"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dennis L. Goeckel","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Massachusetts, Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Massachusetts, Amherst","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4458,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87458034,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1677","last_page":"1681"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision 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":0.6667415499687195},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5923545956611633},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5882625579833984},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.5733805298805237},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5316598415374756},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5192323327064514},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.49508777260780334},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38886481523513794},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26157015562057495},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2556086778640747},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18939781188964844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6667415499687195},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5923545956611633},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5882625579833984},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.5733805298805237},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5316598415374756},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5192323327064514},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.49508777260780334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38886481523513794},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26157015562057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2556086778640747},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18939781188964844},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2017.8335645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2017.8335645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1917532482","https://openalex.org/W1969890816","https://openalex.org/W1997127735","https://openalex.org/W2061204737","https://openalex.org/W2133177257","https://openalex.org/W2195256693","https://openalex.org/W2568631573","https://openalex.org/W2742257485","https://openalex.org/W3099827099","https://openalex.org/W3103861542"],"related_works":["https://openalex.org/W1619264321","https://openalex.org/W4388311650","https://openalex.org/W1974056099","https://openalex.org/W4245343541","https://openalex.org/W2386077341","https://openalex.org/W563589758","https://openalex.org/W5922282","https://openalex.org/W2954004777","https://openalex.org/W2949638731","https://openalex.org/W4293273057"],"abstract_inverted_index":{"A":[0],"user's":[1],"privacy":[2],"can":[3],"be":[4],"compromised":[5],"by":[6],"matching":[7,27],"the":[8,21,33,47,50,53,56,59,62,86,106,138,150,161,164,172,184],"statistical":[9],"characteristics":[10,57],"of":[11,15,20,52,58,135,163,175,186],"an":[12,132],"anonymized":[13],"trace":[14],"interest":[16],"to":[17,45,80,160,170,182],"prior":[18],"behavior":[19,174],"user.":[22],"Here,":[23],"we":[24,71,155,178],"address":[25],"this":[26],"problem":[28],"from":[29,41,110,124,142],"first":[30],"principles":[31],"in":[32,92,137],"Bayesian":[34],"case,":[35],"where":[36,105,119],"user":[37,65,68,130],"parameters":[38],"are":[39,108,122],"drawn":[40,109,123,141],"a":[42,98,111,113,125,145],"known":[43],"distribution,":[44,127],"understand":[46],"relationship":[48],"between":[49,64],"length":[51],"observed":[54],"traces,":[55],"distribution":[60],"defining":[61],"differences":[63],"behavior,":[66],"and":[67,78,166],"privacy.":[69,176],"First,":[70],"establish":[72],"optimal":[73],"tests":[74,165],"(of":[75],"two":[76],"hypotheses":[77,82],"extended":[79],"multiple":[81],"as":[83],"well)":[84],"for":[85,102,149],"cases":[87],"with:":[88],"1)":[89],"continuous":[90],"alphabets,":[91],"particular":[93],"i.i.d.":[94,120],"Gaussian":[95,153],"observations":[96,121],"with":[97,128,152],"different":[99],"(unknown)":[100,133],"mean":[101],"each":[103,129],"user,":[104],"means":[107],"general":[112,157],"priori":[114,146],"distribution;":[115],"2)":[116],"binary":[117],"alphabets":[118],"Bernoulli":[126],"having":[131],"probability":[134],"being":[136],"\"0\"":[139],"state":[140],"some":[143],"certain":[144],"distribution.":[147],"Next,":[148],"case":[151],"observations,":[154],"provide":[156],"(non-asymptotic)":[158],"bounds":[159],"performance":[162],"also":[167],"employ":[168],"these":[169],"show":[171],"scaling":[173],"Finally,":[177],"present":[179],"simulation":[180],"results":[181],"demonstrate":[183],"accuracy":[185],"our":[187],"analytical":[188],"bounds.":[189]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
