Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey
Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed...
Uloženo v:
| Vydáno v: | IEEE internet of things journal Ročník 8; číslo 13; s. 10412 - 10429 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2327-4662, 2327-4662 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed model structures. However, privacy concerns raise from such necessities. First, as the training data are usually distributed among multiple parties, directly exposing and collecting such large amount of data could violate the laws especially for private information, such as personal identities, medical records, and financial profiles. Second, locally deploying advantageous computation resources is costly for individual party having partial data. Third, direct release of well-trained model parameters threatens the information about training data or the intellectual property of model owners. Therefore, individual party prefers outsourcing computation (data) in a secure way to powerful cloud servers such as Microsoft Azure, and how to enable the cloud servers to perform DL algorithms without revealing data owners' private information and model owners' valuable parameters is emerging as an urgent task, which is termed as privacy-preserving (outsourcing) DL. In this article, we review the state-of-the-art researches in privacy-preserving DL based on multiparty secure computation with data encryption and summarize these techniques in both training phase and inference phase. Specifically, we categorize the techniques with respect to the linear and nonlinear computations, which are the two basic building blocks in DL. Following a comprehensive overview of each research scheme, we present primary technical hurdles needed to be addressed and discuss several promising directions for future research. |
|---|---|
| AbstractList | Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed model structures. However, privacy concerns raise from such necessities. First, as the training data are usually distributed among multiple parties, directly exposing and collecting such large amount of data could violate the laws especially for private information, such as personal identities, medical records, and financial profiles. Second, locally deploying advantageous computation resources is costly for individual party having partial data. Third, direct release of well-trained model parameters threatens the information about training data or the intellectual property of model owners. Therefore, individual party prefers outsourcing computation (data) in a secure way to powerful cloud servers such as Microsoft Azure, and how to enable the cloud servers to perform DL algorithms without revealing data owners’ private information and model owners’ valuable parameters is emerging as an urgent task, which is termed as privacy-preserving (outsourcing) DL. In this article, we review the state-of-the-art researches in privacy-preserving DL based on multiparty secure computation with data encryption and summarize these techniques in both training phase and inference phase. Specifically, we categorize the techniques with respect to the linear and nonlinear computations, which are the two basic building blocks in DL. Following a comprehensive overview of each research scheme, we present primary technical hurdles needed to be addressed and discuss several promising directions for future research. |
| Author | Wu, Hongyi Xin, Chunsheng Zhang, Qiao |
| Author_xml | – sequence: 1 givenname: Qiao orcidid: 0000-0002-7752-0528 surname: Zhang fullname: Zhang, Qiao email: qzhan002@odu.edu organization: School of Cybersecurity and the Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA – sequence: 2 givenname: Chunsheng orcidid: 0000-0001-5575-2849 surname: Xin fullname: Xin, Chunsheng email: cxin@odu.edu organization: School of Cybersecurity and the Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA – sequence: 3 givenname: Hongyi surname: Wu fullname: Wu, Hongyi email: h1wu@odu.edu organization: School of Cybersecurity and the Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA |
| BookMark | eNp9kE1Lw0AQhhdRsNb-APGy4Dl1P5JN4q3Wr0qlldbzskknsqVN4u6mkH_vhhQRDzKHmWHmeWd4L9BpWZWA0BUlY0pJevs6W6zHjDA65iRKBE9O0IBxFgehEOz0V32ORtZuCSEei2gqBuh9afRB5W2wNGDBHHT5iR8AajwHZcquu1cWNrgq8Vuzc7pWxrV4BXljAE-rfd045XRV3uEJXjXmAO0lOivUzsLomIfo4-lxPX0J5ovn2XQyD3KWcheEipMsUiSGrBAR-EhFlBEWJzTiIhExpzTNFYQCsqTYpCoLIQE_Zhz8nPIhuul1a1N9NWCd3FaNKf1JyaIwZF7OHxqiuN_KTWWtgULmuv_YGaV3khLZWSg7C2VnoTxa6En6h6yN3ivT_stc94wGgJ_9lPtPBOHfUFZ9Lw |
| CODEN | IITJAU |
| CitedBy_id | crossref_primary_10_1109_JIOT_2025_3540465 crossref_primary_10_3390_foods13213391 crossref_primary_10_1007_s11831_025_10355_z crossref_primary_10_1109_ACCESS_2025_3561721 crossref_primary_10_1109_JIOT_2025_3555026 crossref_primary_10_1145_3703452 crossref_primary_10_1109_ACCESS_2025_3557228 crossref_primary_10_1109_TETCI_2024_3523771 crossref_primary_10_1145_3708505 crossref_primary_10_32604_cmes_2024_048932 crossref_primary_10_1016_j_compeleceng_2024_109792 crossref_primary_10_1016_j_ject_2025_08_001 crossref_primary_10_1109_ACCESS_2024_3381611 crossref_primary_10_1016_j_comnet_2024_110641 crossref_primary_10_1109_COMST_2021_3108618 crossref_primary_10_1109_JIOT_2024_3415499 crossref_primary_10_1016_j_dcan_2025_04_009 |
| Cites_doi | 10.1109/TIP.2017.2670780 10.1080/15265161.2010.494215 10.1109/JIOT.2018.2838574 10.1145/2508859.2516738 10.1145/3133956.3134056 10.1007/978-3-642-40041-4_5 10.1109/SFCS.1986.25 10.1145/2487726.2488368 10.1109/SP.2017.15 10.1371/journal.pone.0168054 10.1109/SP.2019.00045 10.1007/978-3-319-57959-7 10.1007/s00779-016-0963-3 10.1109/SP40000.2020.00092 10.1515/popets-2018-0024 10.1016/j.ins.2018.12.015 10.1109/FOCS.2010.12 10.1109/MSP.2012.2205597 10.1145/3133956.3134077 10.1109/TII.2018.2875149 10.1109/TII.2018.2853676 10.1145/359168.359176 10.1109/TASL.2011.2134090 10.1109/SP.2013.39 10.1145/3319535.3339819 10.1109/ICDE48307.2020.00152 10.1007/978-3-642-20465-4_4 10.1145/3373376.3378523 10.1145/3065913.3065915 10.1145/3310273.3323047 10.1109/SP.2017.12 10.1016/j.clsr.2013.07.010 10.1145/3319535.3363207 10.14722/ndss.2020.23005 10.1145/2647868.2654889 10.1007/978-3-540-70583-3_40 10.1145/3411501.3419425 10.1145/3195970.3196023 10.1109/COMST.2018.2844341 10.1145/2633600 10.1561/3300000019 10.1109/JPROC.2020.2976475 10.1145/3372297.3417872 10.1109/MSEC.2018.2888775 10.1007/3-540-48910-X_16 10.1109/TDSC.2019.2913362 10.1561/0400000042 10.1109/TPAMI.2019.2944377 10.1109/EuroSP.2016.28 10.1145/100216.100287 10.1109/JIOT.2018.2875244 10.1145/1077464.1077466 10.1007/978-3-319-70694-8_15 10.1006/jcom.1998.0476 10.1145/3007787.3001163 10.1109/IEMBS.2006.260060 10.1109/HPCA47549.2020.00030 10.1109/SP.2017.41 10.1109/FCCM48280.2020.00037 10.1109/CVPR.2016.90 10.2478/popets-2019-0035 10.1007/978-3-540-45146-4_9 10.1145/3243734.3243837 10.1016/j.neucom.2019.11.041 10.1109/TNNLS.2018.2886017 10.1109/ICASSP.2013.6639344 10.1007/978-3-642-32009-5_50 10.24963/ijcai.2019/671 10.1007/978-3-540-79228-4_1 10.1109/CVPR.2015.7298594 10.1145/2810103.2813677 10.1109/ICDCS.2018.00178 10.1109/CVPR.2017.243 10.1145/2608628.2608664 10.1145/257874.257896 10.24963/ijcai.2018/547 10.1007/978-3-642-40994-3_25 10.1109/TNSE.2018.2846736 10.1145/3394658 10.1109/CVPR.2009.5206848 10.1007/s00145-011-9107-0 10.1109/TIT.1985.1057074 10.1007/s10623-012-9720-4 10.1007/978-3-319-22846-4_11 10.1145/3196494.3196522 10.1145/1536414.1536440 10.1145/3372297.3417274 10.1007/978-3-030-30619-9_4 10.1007/978-3-319-70972-7_27 10.1109/JIOT.2017.2720635 10.14722/ndss.2015.23241 10.1109/SP.2019.00028 10.1007/978-3-540-30576-7_18 10.1109/TIFS.2020.2988132 10.1145/2976749.2978331 10.1145/3158363 10.1145/28395.28420 10.1109/ACCESS.2018.2830661 10.1007/978-3-662-46803-6_8 10.1109/ICMLA.2015.152 10.1145/2948618.2954331 10.1145/3338469.3358944 10.1109/SFCS.1982.38 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/JIOT.2021.3058638 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2327-4662 |
| EndPage | 10429 |
| ExternalDocumentID | 10_1109_JIOT_2021_3058638 9352960 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Science Foundation grantid: CNS-1828593; OAC-1829771; EEC-1840458; CNS-1950704 funderid: 10.13039/100000001 – fundername: Office of Naval Research grantid: N00014-20-1-2065 funderid: 10.13039/100000006 – fundername: Commonwealth Cyber Initiative, an Investment in the Advancement of Cyber Research and Development, Innovation and Workforce Development |
| GroupedDBID | 0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS IFIPE IPLJI JAVBF M43 OCL PQQKQ RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c293t-4a30b5a07ebf65e5e5965b027815368673119cae46eb8fd9ab4e8e27823e36813 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 27 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000665207100017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2327-4662 |
| IngestDate | Sun Nov 30 04:45:06 EST 2025 Tue Nov 18 21:12:15 EST 2025 Sat Nov 29 06:16:56 EST 2025 Wed Aug 27 02:27:01 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 13 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c293t-4a30b5a07ebf65e5e5965b027815368673119cae46eb8fd9ab4e8e27823e36813 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-7752-0528 0000-0001-5575-2849 |
| PQID | 2544296529 |
| PQPubID | 2040421 |
| PageCount | 18 |
| ParticipantIDs | proquest_journals_2544296529 crossref_citationtrail_10_1109_JIOT_2021_3058638 ieee_primary_9352960 crossref_primary_10_1109_JIOT_2021_3058638 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-07-01 |
| PublicationDateYYYYMMDD | 2021-07-01 |
| PublicationDate_xml | – month: 07 year: 2021 text: 2021-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE internet of things journal |
| PublicationTitleAbbrev | JIoT |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 (ref96) 2020 ref59 boneh (ref53) 2005 liu (ref48) 2020 ref58 wagh (ref132) 2018 ref55 elgamal (ref52) 1985; 31 ref169 ref54 kozlov (ref181) 2020 (ref99) 2020 koti (ref129) 2020 liu (ref135) 2020 zhu (ref139) 2020 bourse (ref167) 2017 (ref102) 2020 ref175 ref51 ref50 ref173 ref174 ref172 biondo (ref119) 2018 ref45 (ref168) 2020 dahl (ref189) 2018 ref47 (ref70) 2020 ref41 ref43 (ref30) 2018 (ref107) 2020 ref180 ref7 ref4 simonyan (ref9) 2014 dwork (ref44) 2006 ref5 ref40 ref187 ref184 (ref73) 2020 ref185 ref182 ref183 banerjee (ref156) 2020 ref35 (ref61) 2020 ref34 wagh (ref121) 2020 ref37 (ref71) 2020 ref36 hashemi (ref157) 2020 (ref104) 2020 froelicher (ref150) 2020 ref33 ref147 ref38 dwork (ref42) 2014; 9 (ref108) 2020 (ref31) 2019 aloufi (ref49) 2020 (ref94) 2020 (ref170) 2020 ref153 ref154 ref152 han (ref179) 2015 ref24 ref23 (ref67) 2020 ref26 badawi (ref151) 2018 morshed (ref176) 2020 ref25 ref20 ref22 ref21 ioffe (ref163) 2015 fan (ref56) 2012 chabanne (ref155) 2017 ref27 evans (ref160) 2017; 2 (ref103) 2020 (ref95) 2020 bashar (ref6) 2019; 1 tramèr (ref123) 2016 han (ref178) 2015 ref166 juvekar (ref76) 2018 ref164 (ref109) 2020 (ref32) 2019 (ref171) 2020 mirshghallah (ref39) 2020 gilad-bachrach (ref142) 2016 ref161 tramèr (ref158) 2018 ref13 ref12 ref128 ref15 ref14 ref126 ref127 zhu (ref122) 2020 ref124 ref11 ref125 ref10 snyder (ref85) 2014 zhao (ref186) 2020 ref17 ref16 ref19 ref18 ref133 ref93 (ref72) 2020 ref134 reagen (ref141) 2020 ref92 ref131 ref130 ref91 riazi (ref138) 2019 ref137 ref86 (ref62) 2020 ref88 ref87 (ref29) 1996 goldreich (ref89) 2009 aggarwal (ref136) 2020 (ref65) 2020 wang (ref162) 2016 li (ref145) 2020 ref144 ref82 (ref116) 2020 abadi (ref165) 2016 ref81 ref84 reis (ref177) 2020 ref83 (ref101) 2020 ref140 zhang (ref146) 2019 ref80 ref78 adshead (ref3) 2014; 9 lecun (ref46) 1990 (ref68) 2020 ref77 chou (ref143) 2018 brasser (ref117) 2017 ref2 ref1 ref191 ref192 ref190 krizhevsky (ref8) 2012 bian (ref149) 2020 (ref74) 2020 ref111 (ref100) 2020 ref112 (ref97) 2020 ref69 ref118 ref64 ref115 ref66 ref113 ref114 ryffel (ref188) 2018 hunt (ref159) 2018 lou (ref148) 2020 (ref75) 2020 keller (ref110) 2020 (ref98) 2020 mohassel (ref105) 2018 ref60 beaver (ref90) 1991 goldman (ref28) 2020 (ref106) 2020 ref120 mishra (ref79) 2020 (ref63) 2020 |
| References_xml | – ident: ref13 doi: 10.1109/TIP.2017.2670780 – ident: ref24 doi: 10.1080/15265161.2010.494215 – year: 2020 ident: ref94 – ident: ref17 doi: 10.1109/JIOT.2018.2838574 – ident: ref161 doi: 10.1145/2508859.2516738 – ident: ref131 doi: 10.1145/3133956.3134056 – ident: ref66 doi: 10.1007/978-3-642-40041-4_5 – year: 2020 ident: ref104 – ident: ref120 doi: 10.1109/SFCS.1986.25 – year: 2020 ident: ref73 – ident: ref112 doi: 10.1145/2487726.2488368 – ident: ref92 doi: 10.1109/SP.2017.15 – ident: ref172 doi: 10.1371/journal.pone.0168054 – year: 2020 ident: ref107 – ident: ref153 doi: 10.1109/SP.2019.00045 – ident: ref26 doi: 10.1007/978-3-319-57959-7 – year: 2020 ident: ref181 publication-title: Neural network compression framework for fast model inference – ident: ref21 doi: 10.1007/s00779-016-0963-3 – year: 2020 ident: ref116 – ident: ref187 doi: 10.1109/SP40000.2020.00092 – ident: ref154 doi: 10.1515/popets-2018-0024 – ident: ref152 doi: 10.1016/j.ins.2018.12.015 – year: 2019 ident: ref146 publication-title: CHEETAH An ultra-fast approximation-free and privacy-preserved neural network framework based on joint obscure linear and nonlinear computations – start-page: 201 year: 2016 ident: ref142 article-title: CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy publication-title: Proc Int Conf Mach Learn – year: 2020 ident: ref39 publication-title: Privacy in deep learning A survey – ident: ref43 doi: 10.1109/FOCS.2010.12 – ident: ref10 doi: 10.1109/MSP.2012.2205597 – start-page: 9403 year: 2020 ident: ref149 article-title: ENSEI: Efficient secure inference via frequency-domain homomorphic convolution for privacy-preserving visual recognition publication-title: Proc IEEE Conf Comput Vis and Pattern Recog – year: 2014 ident: ref85 article-title: Yao's garbled circuits: Recent directions and implementations publication-title: Literature Review – ident: ref33 doi: 10.1145/3133956.3134077 – start-page: 1213 year: 2018 ident: ref119 article-title: The guard's dilemma: Efficient code-reuse attacks against Intel SGX publication-title: Proc 27th USENIX Security Symp – year: 2017 ident: ref167 article-title: Fast homomorphic evaluation of deep discretized neural networks – volume: 9 year: 2014 ident: ref3 publication-title: Data set to grow 10-fold by 2020 as internet of things takes off – year: 2009 ident: ref89 publication-title: Foundations of Cryptography Volume 2 Basic Applications – ident: ref4 doi: 10.1109/TII.2018.2875149 – ident: ref5 doi: 10.1109/TII.2018.2853676 – year: 2020 ident: ref136 publication-title: SOTERIA In search of efficient neural networks for private inference – ident: ref88 doi: 10.1145/359168.359176 – start-page: 396 year: 1990 ident: ref46 article-title: Handwritten digit recognition with a back-propagation network publication-title: Advances in neural information processing systems – year: 2018 ident: ref159 publication-title: Chiron Privacy-preserving machine learning as a service – ident: ref11 doi: 10.1109/TASL.2011.2134090 – ident: ref82 doi: 10.1109/SP.2013.39 – ident: ref84 doi: 10.1145/3319535.3339819 – ident: ref147 doi: 10.1109/ICDE48307.2020.00152 – ident: ref69 doi: 10.1007/978-3-642-20465-4_4 – year: 2020 ident: ref170 – ident: ref175 doi: 10.1145/3373376.3378523 – year: 2020 ident: ref61 – ident: ref118 doi: 10.1145/3065913.3065915 – ident: ref191 doi: 10.1145/3310273.3323047 – ident: ref128 doi: 10.1109/SP.2017.12 – year: 2016 ident: ref162 publication-title: EMP-toolkit Efficient multiparty computation toolkit – ident: ref27 doi: 10.1016/j.clsr.2013.07.010 – ident: ref59 doi: 10.1145/3319535.3363207 – ident: ref133 doi: 10.14722/ndss.2020.23005 – start-page: 2505 year: 2020 ident: ref79 article-title: Delphi: A cryptographic inference service for neural networks publication-title: Proc 29th USENIX Security Symp – ident: ref47 doi: 10.1145/2647868.2654889 – year: 2014 ident: ref9 publication-title: Very Deep Convolutional Networks for Large-scale Image Recognition – start-page: 1501 year: 2019 ident: ref138 article-title: XONN XNOR-based oblivious deep neural network inference publication-title: Proc 28th USENIX Security Symp – ident: ref80 doi: 10.1007/978-3-540-70583-3_40 – year: 2020 ident: ref100 – year: 2020 ident: ref63 – year: 2020 ident: ref129 publication-title: SWIFT Super-fast and robust privacy-preserving machine learning – year: 2020 ident: ref148 publication-title: AutoPrivacy Automated layer-wise parameter selection for secure neural network inference – year: 2018 ident: ref132 article-title: Securenn: Efficient and private neural network training – ident: ref192 doi: 10.1145/3411501.3419425 – ident: ref137 doi: 10.1145/3195970.3196023 – ident: ref16 doi: 10.1109/COMST.2018.2844341 – year: 2018 ident: ref158 publication-title: Slalom Fast verifiable and private execution of neural networks in trusted hardware – year: 2015 ident: ref178 publication-title: Deep compression Compressing deep neural networks with pruning trained quantization and huffman coding – ident: ref64 doi: 10.1145/2633600 – year: 2018 ident: ref30 publication-title: Uber to Pay 148 Million Penalty to Settle 2016 Data Breach – year: 2019 ident: ref32 publication-title: France Fines Google 57 Million for Breaking Europe's Strict New Privacy Rules – year: 2020 ident: ref67 – volume: 2 start-page: 70 year: 2017 ident: ref160 article-title: A pragmatic introduction to secure multi-party computation publication-title: Foundations and Trends in Privacy and Security doi: 10.1561/3300000019 – year: 2020 ident: ref97 – year: 2020 ident: ref28 article-title: An introduction to the California consumer privacy act (CCPA) publication-title: Santa Clara Univ Legal Studies Research Paper – ident: ref45 doi: 10.1109/JPROC.2020.2976475 – year: 2020 ident: ref110 article-title: MP-SPDZ: A versatile framework for multi-party computation doi: 10.1145/3372297.3417872 – start-page: 265 year: 2006 ident: ref44 article-title: Calibrating noise to sensitivity in private data analysis publication-title: Proc Theory Cryptogr Conf – year: 2020 ident: ref71 – year: 2020 ident: ref121 publication-title: Falcon Honest-majority maliciously secure framework for private deep learning – ident: ref37 doi: 10.1109/MSEC.2018.2888775 – ident: ref51 doi: 10.1007/3-540-48910-X_16 – year: 2020 ident: ref176 publication-title: CPU and GPU accelerated fully homomorphic encryption – year: 2020 ident: ref102 – year: 2020 ident: ref95 – ident: ref134 doi: 10.1109/TDSC.2019.2913362 – year: 2020 ident: ref139 article-title: Practical MPC+FHE with applications in secure multi-partyneural network evaluation – year: 2020 ident: ref65 – volume: 9 start-page: 211 year: 2014 ident: ref42 article-title: The algorithmic foundations of differential privacy publication-title: Found Trends Theor Comput Sci doi: 10.1561/0400000042 – ident: ref14 doi: 10.1109/TPAMI.2019.2944377 – ident: ref115 doi: 10.1109/EuroSP.2016.28 – ident: ref81 doi: 10.1145/100216.100287 – start-page: 1651 year: 2018 ident: ref76 article-title: GAZELLE: A low latency framework for secure neural network inference publication-title: Proc 27th USENIX Security Symp – year: 2020 ident: ref49 article-title: Computing blindfolded on data homomorphically encrypted under multiple keys: An extended survey – year: 2020 ident: ref156 publication-title: SESAME Software defined enclaves to secure inference accelerators with multi-tenant execution – ident: ref2 doi: 10.1109/JIOT.2018.2875244 – ident: ref184 doi: 10.1145/1077464.1077466 – year: 2020 ident: ref150 publication-title: Scalable privacy-preserving distributed learning – year: 2020 ident: ref99 – ident: ref57 doi: 10.1007/978-3-319-70694-8_15 – year: 2020 ident: ref141 publication-title: Cheetah Optimizing and accelerating homomorphic encryption for private inference – ident: ref185 doi: 10.1006/jcom.1998.0476 – ident: ref180 doi: 10.1145/3007787.3001163 – year: 2020 ident: ref109 – ident: ref23 doi: 10.1109/IEMBS.2006.260060 – year: 2020 ident: ref171 – ident: ref182 doi: 10.1109/HPCA47549.2020.00030 – ident: ref124 doi: 10.1109/SP.2017.41 – ident: ref174 doi: 10.1109/FCCM48280.2020.00037 – ident: ref19 doi: 10.1109/CVPR.2016.90 – year: 2018 ident: ref143 publication-title: Faster CryptoNets Leveraging sparsity for real-world encrypted inference – ident: ref35 doi: 10.2478/popets-2019-0035 – ident: ref87 doi: 10.1007/978-3-540-45146-4_9 – ident: ref50 doi: 10.1145/3243734.3243837 – ident: ref40 doi: 10.1016/j.neucom.2019.11.041 – ident: ref125 doi: 10.1109/TNNLS.2018.2886017 – ident: ref12 doi: 10.1109/ICASSP.2013.6639344 – volume: 1 start-page: 73 year: 2019 ident: ref6 article-title: Survey on evolving deep learning neural network architectures publication-title: J Artif Intell – start-page: 420 year: 1991 ident: ref90 article-title: Efficient multiparty protocols using circuit randomization publication-title: Proc Annu Int Cryptol Conf – ident: ref55 doi: 10.1007/978-3-642-32009-5_50 – ident: ref140 doi: 10.24963/ijcai.2019/671 – ident: ref41 doi: 10.1007/978-3-540-79228-4_1 – ident: ref18 doi: 10.1109/CVPR.2015.7298594 – start-page: 1135 year: 2015 ident: ref179 article-title: Learning both weights and connections for efficient neural network publication-title: Advances in neural information processing systems – year: 2020 ident: ref75 – year: 2020 ident: ref106 – ident: ref34 doi: 10.1145/2810103.2813677 – year: 2020 ident: ref62 – ident: ref15 doi: 10.1109/ICDCS.2018.00178 – year: 2018 ident: ref188 publication-title: A generic framework for privacy preserving deep learning – ident: ref164 doi: 10.1109/CVPR.2017.243 – ident: ref183 doi: 10.1145/2608628.2608664 – ident: ref22 doi: 10.1145/257874.257896 – ident: ref144 doi: 10.24963/ijcai.2018/547 – year: 2020 ident: ref177 publication-title: Computing-in-memory for performance and energy efficient homomorphic encryption – ident: ref126 doi: 10.1007/978-3-642-40994-3_25 – ident: ref25 doi: 10.1109/TNSE.2018.2846736 – year: 2018 ident: ref189 publication-title: Private machine learning in TensorFlow using secure computation – ident: ref60 doi: 10.1145/3394658 – start-page: 601 year: 2016 ident: ref123 article-title: Stealing machine learning models via prediction APIs publication-title: Proc 25th Usenix Security Symp – start-page: 265 year: 2016 ident: ref165 article-title: TensorFlow: A system for large-scale machine learning publication-title: Proc 12th USENIX Symp Oper Syst Design Implement – year: 2012 ident: ref56 article-title: Somewhat practical fully homomorphic encryption – ident: ref169 doi: 10.1109/CVPR.2009.5206848 – ident: ref78 doi: 10.1007/s00145-011-9107-0 – volume: 31 start-page: 469 year: 1985 ident: ref52 article-title: A public key cryptosystem and a signature scheme based on discrete logarithms publication-title: IEEE Trans Inf Theory doi: 10.1109/TIT.1985.1057074 – ident: ref58 doi: 10.1007/s10623-012-9720-4 – year: 2020 ident: ref48 publication-title: MPC-enabled privacy-preserving neural network training against malicious attack – start-page: 8705 year: 2020 ident: ref145 article-title: FALCON: A fourier transform based approach for fast and secure convolutional neural network predictions publication-title: Proc IEEE Conf Comput Vis and Pattern Recog – start-page: 35 year: 2018 ident: ref105 article-title: ABY3: A mixed protocol framework for machine learning publication-title: Proc ACM SIGSAC Conf Comput Commun Security – year: 2020 ident: ref74 – year: 2020 ident: ref157 publication-title: DarKnight A data privacy scheme for training and inference of deep neural networks – ident: ref114 doi: 10.1007/978-3-319-22846-4_11 – year: 2020 ident: ref108 – year: 2020 ident: ref103 – ident: ref130 doi: 10.1145/3196494.3196522 – ident: ref54 doi: 10.1145/1536414.1536440 – ident: ref173 doi: 10.1145/3372297.3417274 – start-page: 1097 year: 2012 ident: ref8 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in neural information processing systems – ident: ref36 doi: 10.1007/978-3-030-30619-9_4 – year: 2015 ident: ref163 publication-title: Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift – ident: ref113 doi: 10.1007/978-3-319-70972-7_27 – year: 2020 ident: ref68 – ident: ref1 doi: 10.1109/JIOT.2017.2720635 – ident: ref166 doi: 10.14722/ndss.2015.23241 – year: 2020 ident: ref98 – start-page: 11 year: 2017 ident: ref117 article-title: Software grand exposure: SGX cache attacks are practical publication-title: Proc 11th USENIX Workshop Offensive Technol – year: 2020 ident: ref72 – year: 2020 ident: ref168 – ident: ref93 doi: 10.1109/SP.2019.00028 – year: 2017 ident: ref155 article-title: Privacy-preserving classification on deep neural network – start-page: 325 year: 2005 ident: ref53 article-title: Evaluating 2-DNF formulas on ciphertexts publication-title: Proc Theory Cryptogr Conf doi: 10.1007/978-3-540-30576-7_18 – ident: ref127 doi: 10.1109/TIFS.2020.2988132 – year: 2020 ident: ref135 article-title: Leia: A lightweight cryptographic neural network inference system at the edge – year: 2020 ident: ref101 – ident: ref91 doi: 10.1145/2976749.2978331 – year: 2020 ident: ref70 – ident: ref38 doi: 10.1145/3158363 – year: 2020 ident: ref96 – year: 2020 ident: ref186 publication-title: Efficient integer-arithmetic-only convolutional neural networks – year: 2018 ident: ref151 publication-title: The alexnet moment for homomorphic encryption Hcnn the first homomorphic cnn on encrypted data with gpus – year: 2019 ident: ref31 publication-title: Data Breach News PDPC Fines IHiS SingHealth – ident: ref86 doi: 10.1145/28395.28420 – ident: ref7 doi: 10.1109/ACCESS.2018.2830661 – ident: ref83 doi: 10.1007/978-3-662-46803-6_8 – ident: ref20 doi: 10.1109/ICMLA.2015.152 – ident: ref111 doi: 10.1145/2948618.2954331 – ident: ref190 doi: 10.1145/3338469.3358944 – ident: ref77 doi: 10.1109/SFCS.1982.38 – year: 2020 ident: ref122 publication-title: Hermes attack Steal DNN models with lossless inference accuracy – year: 1996 ident: ref29 article-title: Health insurance portability and accountability act of 1996 publication-title: Public Law 104-191 |
| SSID | ssj0001105196 |
| Score | 2.4097583 |
| Snippet | Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 10412 |
| SubjectTerms | Algorithms Cloud computing Computational modeling Data encryption Data models Data privacy Deep learning Deep learning (DL) Image classification linear and nonlinear computations Machine learning Mathematical models Object recognition Outsourcing Parameters Predictive models Privacy privacy preserving Servers Speech recognition State-of-the-art reviews Training |
| Title | Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey |
| URI | https://ieeexplore.ieee.org/document/9352960 https://www.proquest.com/docview/2544296529 |
| Volume | 8 |
| WOSCitedRecordID | wos000665207100017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2327-4662 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001105196 issn: 2327-4662 databaseCode: RIE dateStart: 20140101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlR3LSgMxMNTiwYtVq1itkoMnMXWf2cRbfRT1UCtW6G1JsrOlINvSF_TvTbJpFRRB9hKYSVhmksxM5oXQhYKcxaB8woTKSaR1cMK9QBCuRTVLgjAUkbTNJpJulw0GvFdBV5tcGACwwWfQMkPry8_GamGeyq55aLyE2kDfShJa5mp9vaf4RhmhznHpe_z6-emlrw3AwG_pPc2oyUD5JnpsL5UfF7CVKp3a__5nD-067RG3S3bvowoUB6i27syA3UGto9fedLQUakVMhIW5DYohvgeYYFdOdYhvtfTK8LjANgN3ojfQCtu3d8DlcpZjN7iN3xbTJawO0XvnoX_3SFzzBKK0BJ-TSISejIWXgMxpDPrjNJbGz6jvOMpoEvo-VwIiCpLlGRcyAgYaHISg4X54hKrFuIBjhHMuqZdFIguVPvLM40ESMOpDImOtfwBrIG9N11S5yuKmwcVHai0Mj6eGFalhRepY0UCXmymTsqzGX8h1Q_sNoiN7AzXXzEvdwZulpuKaBmqEk99nnaIds3YZcdtE1fl0AWdoWy3no9n03O6pT_6wyTQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlR3LSgMxcCgq6MVXFeszB0_iavaVTbz5pL5qxQrelmx2VgTZltoW-vcm2bQKiiB7WchkN2RmMq_MDMC-woLHqHyPS1V4kdbBPUED6QktqnkShKGMMttsImm1-MuLaNfgcJoLg4j28hkemVcby8-7amhcZcciNFFCbaDPxlEU0Cpb68uj4ht1hLnQpU_F8c31Q0ebgIF_pKmaM5OD8k342G4qP45gK1eulv63omVYdPojOa0QvgI1LFdhadKbgThWrcNju_82kmrsmTsW5jwoX8kFYo-4gqqv5EzLr5x0S2JzcHuahMbEet-RVJ-zODshp-Rp2B_heA2ery47503PtU_wlJbhAy-SIc1iSRPMChajfgSLMxNp1Kcc4ywJfV8oiRHDjBe5kFmEHPVwEKIe98N1mCm7JW4AKUTGaB7JPFSa6TkVQRJw5mOSxVoDQd4AOtnXVLna4qbFxXtqbQwqUoOK1KAidahowMF0Sq8qrPEXcN3s_RTQbXsDtifISx3rfaSm5poe1ACbv8_ag_lm5_4uvbtu3W7BgvlPdf92G2YG_SHuwJwaDd4--ruWvj4BoC_Mew |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Privacy-Preserving+Deep+Learning+Based+on+Multiparty+Secure+Computation%3A+A+Survey&rft.jtitle=IEEE+internet+of+things+journal&rft.au=Zhang%2C+Qiao&rft.au=Xin%2C+Chunsheng&rft.au=Wu%2C+Hongyi&rft.date=2021-07-01&rft.issn=2327-4662&rft.eissn=2327-4662&rft.volume=8&rft.issue=13&rft.spage=10412&rft.epage=10429&rft_id=info:doi/10.1109%2FJIOT.2021.3058638&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JIOT_2021_3058638 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2327-4662&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2327-4662&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2327-4662&client=summon |