{"id":"https://openalex.org/W4411010458","doi":"https://doi.org/10.1145/3714393.3726505","title":"Secure and Efficient Video Inferences with Compressed 3-Dimensional Deep Neural Networks","display_name":"Secure and Efficient Video Inferences with Compressed 3-Dimensional Deep Neural Networks","publication_year":2024,"publication_date":"2024-06-19","ids":{"openalex":"https://openalex.org/W4411010458","doi":"https://doi.org/10.1145/3714393.3726505"},"language":"en","primary_location":{"id":"doi:10.1145/3714393.3726505","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714393.3726505","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3714393.3726505","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3714393.3726505","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061302407","display_name":"Bingyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I198034347","display_name":"Wentworth Institute of Technology","ror":"https://ror.org/03tqeft14","country_code":"US","type":"education","lineage":["https://openalex.org/I198034347"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bingyu Liu","raw_affiliation_strings":["Wentworth Institute of Technology, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0009-0006-6847-6664","affiliations":[{"raw_affiliation_string":"Wentworth Institute of Technology, Boston, MA, USA","institution_ids":["https://openalex.org/I198034347"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014251325","display_name":"Ali Aras","orcid":"https://orcid.org/0000-0001-8111-6176"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Arastehfard","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":"https://orcid.org/0000-0001-8111-6176","affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073855975","display_name":"Rujia Wang","orcid":"https://orcid.org/0000-0003-4019-5327"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rujia Wang","raw_affiliation_strings":["Microsoft, Redmond, USA"],"raw_orcid":"https://orcid.org/0000-0003-4019-5327","affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101802649","display_name":"Weiran Liu","orcid":"https://orcid.org/0000-0002-1466-7418"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiran Liu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1466-7418","affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072485378","display_name":"Zhongjie Ba","orcid":"https://orcid.org/0000-0003-0921-8869"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongjie Ba","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0921-8869","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070625162","display_name":"Shanglin Zhou","orcid":"https://orcid.org/0000-0002-6409-7716"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shanglin Zhou","raw_affiliation_strings":["University of Connecticut, Storrs, USA"],"raw_orcid":"https://orcid.org/0000-0002-6409-7716","affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100725148","display_name":"Yuan Hong","orcid":"https://orcid.org/0000-0003-4095-4506"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Hong","raw_affiliation_strings":["University of Connecticut, Storrs, USA"],"raw_orcid":"https://orcid.org/0000-0003-4095-4506","affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"185","last_page":"196"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991000294685364,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991000294685364,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9976999759674072,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7438586950302124},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5276687741279602},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5052874684333801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4726509153842926}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438586950302124},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5276687741279602},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5052874684333801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4726509153842926}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3714393.3726505","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714393.3726505","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3714393.3726505","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3714393.3726505","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3714393.3726505","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3714393.3726505","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1126845688","display_name":"Collaborative Research: SaTC: CORE: Small: Privately Collecting and Analyzing V2X Data for Urban Traffic Modeling","funder_award_id":"2302689","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2479209632","display_name":"CAREER: Privacy Preserving Security Analytics: When Security Meets Privacy","funder_award_id":"2308730","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G345098674","display_name":null,"funder_award_id":"CNS-2302689","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3832708380","display_name":"Collaborative Research: Data Poisoning Attacks and Infrastructure-Enabled Solutions for Traffic State Estimation and Prediction","funder_award_id":"2326341","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4360338996","display_name":null,"funder_award_id":"CMMI-2326341","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5386664498","display_name":"Collaborative Research: CSR: Small:Accurate and Private Multi-Camera Surveillance System on Time-Sensitive Networks","funder_award_id":"2432533","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G554810875","display_name":null,"funder_award_id":"CNS-2308730","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6607657467","display_name":"IMR: MM-1B: Longitudinal End-device based Performance Measurement of Cellular Networks with Provable Privacy","funder_award_id":"2319277","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411010458.pdf","grobid_xml":"https://content.openalex.org/works/W4411010458.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2108368547","https://openalex.org/W2108834246","https://openalex.org/W2126579184","https://openalex.org/W2156001253","https://openalex.org/W2164399967","https://openalex.org/W2168325212","https://openalex.org/W2177209050","https://openalex.org/W2402235285","https://openalex.org/W2620512600","https://openalex.org/W2767079719","https://openalex.org/W2768174108","https://openalex.org/W2783769334","https://openalex.org/W2798170643","https://openalex.org/W2805074088","https://openalex.org/W2889746123","https://openalex.org/W2891973500","https://openalex.org/W2917560727","https://openalex.org/W2963106566","https://openalex.org/W2963313259","https://openalex.org/W2963524571","https://openalex.org/W3013394222","https://openalex.org/W3021878953","https://openalex.org/W3036376615","https://openalex.org/W3104263540","https://openalex.org/W3138596485","https://openalex.org/W3163145870","https://openalex.org/W3173524712","https://openalex.org/W3179761913","https://openalex.org/W3192404420","https://openalex.org/W4225586916","https://openalex.org/W4287756467","https://openalex.org/W6600005384","https://openalex.org/W6755519418","https://openalex.org/W6762270585","https://openalex.org/W6784109961"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"network":[2],"(DNN)":[3],"services":[4],"have":[5,58],"been":[6,59],"widely":[7,156],"deployed":[8],"for":[9,73,131,223],"efficient":[10],"and":[11,31,47,65,100,110,150,159,165],"accurate":[12],"inferences":[13,39],"in":[14,52],"many":[15],"different":[16],"domains.":[17],"In":[18],"practice,":[19],"a":[20],"client":[21],"may":[22],"send":[23],"its":[24],"private":[25],"data":[26],"(e.g.,":[27],"images,":[28],"text":[29],"messages":[30],"videos)":[32],"to":[33,36,61,82,133],"the":[34,38,41,89,102,155,163],"service":[35],"get":[37],"with":[40,114,140,152],"proprietary":[42],"DNN":[43],"models.":[44],"Significant":[45],"privacy":[46,64,121],"security":[48,66],"concerns":[49],"would":[50],"emerge":[51],"such":[53,63,95],"scenarios.":[54],"Cryptographic":[55],"inference":[56,84,105,128],"systems":[57,70,129,176],"proposed":[60],"address":[62,94],"concerns.":[67],"However,":[68],"existing":[69,175],"are":[71],"tailored":[72],"DNNs":[74,117],"on":[75,88,137,162,177],"image":[76],"inferences,":[77],"but":[78],"not":[79],"directly":[80],"applicable":[81],"video":[83,135],"tasks":[85],"that":[86,171],"operate":[87],"spatio-temporal":[90],"(3D)":[91],"features.":[92],"To":[93],"critical":[96],"deficiencies,":[97],"we":[98],"design":[99],"implement":[101],"first":[103],"cryptographic":[104,127],"system,":[106],"Crypto3D,":[107],"which":[108],"privately":[109],"efficiently":[111],"infers":[112],"videos":[113],"compressed":[115],"3D":[116,138],"while":[118],"ensuring":[119],"rigorous":[120],"guarantees.":[122],"We":[123,147],"also":[124],"update":[125],"most":[126],"(designed":[130],"images)":[132],"support":[134],"understanding":[136],"features":[139],"non-trivial":[141],"extensions,":[142],"treating":[143],"them":[144],"as":[145],"baselines.":[146],"evaluate":[148],"Crypt3D":[149],"benchmark":[151],"baselines":[153],"utilizing":[154],"adopted":[157],"C3D":[158],"I3D":[160],"models":[161],"UCF-101":[164],"HMDB-51":[166],"datasets.":[167],"Our":[168],"results":[169],"demonstrate":[170],"Crypto3D":[172],"significantly":[173],"outperforms":[174],"execution":[178],"time:":[179],"554.68\u00d7":[180],"vs.":[181,185,189,193,197,202,207,212,216,221],"CryptoDL":[182],"(3D),":[183,187,191,195,200,204,209,214],"189.21\u00d7":[184],"HEANN":[186],"182.61\u00d7":[188],"MP-SPDZ":[190],"133.56\u00d7":[192],"E2DM":[194],"11.09\u00d7":[196],"Intel":[198],"SGX":[199],"8.90\u00d7":[201],"Gazelle":[203],"3.71":[205],"\u00d7":[206,211],"Delphi":[208],"12.97":[210],"CryptFlow2":[213],"1.49\u00d7":[215],"Cheetah":[217],"(3D);":[218],"accuracy:":[219],"82.4%":[220],"<80%":[222],"all":[224],"of":[225],"them.":[226],"Code":[227],"is":[228],"available":[229],"at":[230],"https://github.com/datasec-lab/crypto3D":[231]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}