FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2892226435> ?p ?o ?g. }

• W2892226435 endingPage "1263" @default.
• W2892226435 startingPage "1251" @default.
• W2892226435 abstract "Deep learning has aroused a lot of attention and has been used successfully in many domains, such as accurate image recognition and medical diagnosis. Generally, the training of models requires large, representative datasets, which may be collected from a large number of users and contain sensitive information (e.g., users' photos and medical information). The collected data would be stored and computed by service providers (SPs) or delegated to an untrusted cloud. The users can neither control how it will be used, nor realize what will be learned from it, which make the privacy issues prominent and severe. To solve the privacy issues, one of the most popular approaches is to encrypt users' data with their public keys. However, this technique inevitably leads to another challenge that how to train the model based on multi-key encrypted data. In this paper, we propose a novel privacy-preserving deep learning model, namely PDLM, to apply deep learning over the encrypted data under multiple keys. In PDLM, lots of users contribute their encrypted data to SP to learn a specific model. We adopt an effective privacy-preserving calculation toolkit to achieve the training process based on stochastic gradient descent (SGD) in a privacy-preserving manner. We also prove that our PDLM can achieve users' privacy preservation and analyze the efficiency of PDLM in theory. Finally, we conduct an experiment to evaluate PDLM over two real-world datasets and empirical results demonstrate that our PDLM can effectively and efficiently train the model in a privacy-preserving way." @default.
• W2892226435 created "2018-09-27" @default.
• W2892226435 creator A5012016098 @default.
• W2892226435 creator A5024453724 @default.
• W2892226435 creator A5045333326 @default.
• W2892226435 creator A5065410613 @default.
• W2892226435 creator A5067573004 @default.
• W2892226435 date "2021-07-01" @default.
• W2892226435 modified "2023-10-18" @default.
• W2892226435 title "PDLM: Privacy-Preserving Deep Learning Model on Cloud with Multiple Keys" @default.
• W2892226435 cites W1510952750 @default.
• W2892226435 cites W1528076390 @default.
• W2892226435 cites W1571926107 @default.
• W2892226435 cites W1677182931 @default.
• W2892226435 cites W1964075810 @default.
• W2892226435 cites W2008961301 @default.
• W2892226435 cites W2014103873 @default.
• W2892226435 cites W2014841127 @default.
• W2892226435 cites W2031738616 @default.
• W2892226435 cites W2044857719 @default.
• W2892226435 cites W2053637704 @default.
• W2892226435 cites W2091825929 @default.
• W2892226435 cites W2112796928 @default.
• W2892226435 cites W2132172731 @default.
• W2892226435 cites W2137351756 @default.
• W2892226435 cites W2149058896 @default.
• W2892226435 cites W2194155053 @default.
• W2892226435 cites W2281202115 @default.
• W2892226435 cites W2317339301 @default.
• W2892226435 cites W2405356014 @default.
• W2892226435 cites W2473418344 @default.
• W2892226435 cites W2509467699 @default.
• W2892226435 cites W2538251454 @default.
• W2892226435 cites W2592416275 @default.
• W2892226435 cites W2596777608 @default.
• W2892226435 cites W2597998853 @default.
• W2892226435 cites W2605204252 @default.
• W2892226435 cites W2606937201 @default.
• W2892226435 cites W2620415937 @default.
• W2892226435 cites W2701059868 @default.
• W2892226435 cites W2742912327 @default.
• W2892226435 cites W2753905570 @default.
• W2892226435 cites W2759482137 @default.
• W2892226435 cites W2762867797 @default.
• W2892226435 cites W2767072011 @default.
• W2892226435 cites W2781091734 @default.
• W2892226435 cites W2801491268 @default.
• W2892226435 cites W2911978475 @default.
• W2892226435 cites W2962859574 @default.
• W2892226435 cites W2963313259 @default.
• W2892226435 cites W4248358572 @default.
• W2892226435 doi "https://doi.org/10.1109/tsc.2018.2868750" @default.
• W2892226435 hasPublicationYear "2021" @default.
• W2892226435 type Work @default.
• W2892226435 sameAs 2892226435 @default.
• W2892226435 citedByCount "36" @default.
• W2892226435 countsByYear W28922264352019 @default.
• W2892226435 countsByYear W28922264352020 @default.
• W2892226435 countsByYear W28922264352021 @default.
• W2892226435 countsByYear W28922264352022 @default.
• W2892226435 countsByYear W28922264352023 @default.
• W2892226435 crossrefType "journal-article" @default.
• W2892226435 hasAuthorship W2892226435A5012016098 @default.
• W2892226435 hasAuthorship W2892226435A5024453724 @default.
• W2892226435 hasAuthorship W2892226435A5045333326 @default.
• W2892226435 hasAuthorship W2892226435A5065410613 @default.
• W2892226435 hasAuthorship W2892226435A5067573004 @default.
• W2892226435 hasConcept C108583219 @default.
• W2892226435 hasConcept C111919701 @default.
• W2892226435 hasConcept C119857082 @default.
• W2892226435 hasConcept C123201435 @default.
• W2892226435 hasConcept C124101348 @default.
• W2892226435 hasConcept C148730421 @default.
• W2892226435 hasConcept C154945302 @default.
• W2892226435 hasConcept C206688291 @default.
• W2892226435 hasConcept C26517878 @default.
• W2892226435 hasConcept C38652104 @default.
• W2892226435 hasConcept C41008148 @default.
• W2892226435 hasConcept C50644808 @default.
• W2892226435 hasConcept C79974875 @default.
• W2892226435 hasConcept C98045186 @default.
• W2892226435 hasConceptScore W2892226435C108583219 @default.
• W2892226435 hasConceptScore W2892226435C111919701 @default.
• W2892226435 hasConceptScore W2892226435C119857082 @default.
• W2892226435 hasConceptScore W2892226435C123201435 @default.
• W2892226435 hasConceptScore W2892226435C124101348 @default.
• W2892226435 hasConceptScore W2892226435C148730421 @default.
• W2892226435 hasConceptScore W2892226435C154945302 @default.
• W2892226435 hasConceptScore W2892226435C206688291 @default.
• W2892226435 hasConceptScore W2892226435C26517878 @default.
• W2892226435 hasConceptScore W2892226435C38652104 @default.
• W2892226435 hasConceptScore W2892226435C41008148 @default.
• W2892226435 hasConceptScore W2892226435C50644808 @default.
• W2892226435 hasConceptScore W2892226435C79974875 @default.
• W2892226435 hasConceptScore W2892226435C98045186 @default.
• W2892226435 hasFunder F4320321001 @default.
• W2892226435 hasFunder F4320321543 @default.
• W2892226435 hasFunder F4320321599 @default.

• next