FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3211292012> ?p ?o ?g. }

• W3211292012 abstract "We provide a general framework for secure and private multi-label multi-output machine learning (ML) algorithms for the semi-honest model in distributed edge IoT (Internet of Things) environments enabled by 5G/6G networks. The proposed framework includes the special cases of binary, multi-class and multi-label ML algorithms. We deal with both horizontally and vertically partitioned datasets. Initially, (i) we propose novel secure feature selection protocols by homomorphically evaluating features’ information gains in distributed environments, we proceed with (ii) novel secure training protocols over the set of selected features, then (iii) we propose novel secure building blocks which are commonly used on ML algorithms (e.g. secure sum, comparison, argmax, top-K, sorting, permutation, etc.), as well as on secure linear algebra (e.g. secure inner product, cascading matrix-vector and matrix-matrix multiplications, matrix transpose, etc.), and finally (iv) on top of proposed secure building blocks we build our novel secure ML classification protocols for various ML classifiers such as Deep Neural Networks (DNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests (RF), different flavors of Naive Bayes (NB), Logistic Regression (LR) and K Nearest Neighbors (KNN). Moreover, our secure classification protocols also deal with malicious users that arbitrarily deviate from the protocol and they show no loss of accuracy due to secure classifications. In the process, our participants interact with each other in order to fulfill strict security. privacy and efficiency requirements. To these ends, we provide confidentiality, integrity and authenticity to each interaction by signing their hashed contents with the corresponding participants’ private key. We assure the consistency among interactions by introducing timestamps and linking them with the hashed content(s) of the preceding interaction(s). This makes our protocols a natural fit for blockchain technology. Moreover, the proposed cryptographic tools are proven to be resistant to quantum computer attacks, making our protocols applicable to the post quantum world. We did our theoretical analysis and extensive experimental evaluations over benchmark datasets related to cyber-security and health. They show that our protocols have an advantage ranging from several times to orders of magnitudes with respect to the state-of-the-art in terms of computation and communication costs. This makes our protocols among the most efficient ones in literature. Also, they are among the best in terms of security and privacy properties and allow high rate of fault tolerance and collusion attacks of dataset owners with respect to the state-of-the-art." @default.
• W3211292012 created "2021-11-08" @default.
• W3211292012 creator A5089628605 @default.
• W3211292012 date "2021-01-01" @default.
• W3211292012 modified "2023-09-23" @default.
• W3211292012 title "Blockchain driven secure and private machine learning algorithms for post-quantum 5G/6G enabled industrial IOT with applications to cyber-security and health" @default.
• W3211292012 cites W1530215515 @default.
• W3211292012 cites W1819119697 @default.
• W3211292012 cites W184146824 @default.
• W3211292012 cites W199752024 @default.
• W3211292012 cites W2001084165 @default.
• W3211292012 cites W2012066625 @default.
• W3211292012 cites W2031533839 @default.
• W3211292012 cites W2031738616 @default.
• W3211292012 cites W2076063813 @default.
• W3211292012 cites W2095963390 @default.
• W3211292012 cites W2106463421 @default.
• W3211292012 cites W2132172731 @default.
• W3211292012 cites W2145747124 @default.
• W3211292012 cites W2342408547 @default.
• W3211292012 cites W2435473771 @default.
• W3211292012 cites W2573908344 @default.
• W3211292012 cites W2604462068 @default.
• W3211292012 cites W2614305404 @default.
• W3211292012 cites W2773773675 @default.
• W3211292012 cites W2889746123 @default.
• W3211292012 hasPublicationYear "2021" @default.
• W3211292012 type Work @default.
• W3211292012 sameAs 3211292012 @default.
• W3211292012 citedByCount "0" @default.
• W3211292012 crossrefType "dissertation" @default.
• W3211292012 hasAuthorship W3211292012A5089628605 @default.
• W3211292012 hasConcept C11413529 @default.
• W3211292012 hasConcept C119857082 @default.
• W3211292012 hasConcept C12267149 @default.
• W3211292012 hasConcept C124101348 @default.
• W3211292012 hasConcept C154945302 @default.
• W3211292012 hasConcept C169258074 @default.
• W3211292012 hasConcept C41008148 @default.
• W3211292012 hasConcept C52001869 @default.
• W3211292012 hasConcept C80444323 @default.
• W3211292012 hasConceptScore W3211292012C11413529 @default.
• W3211292012 hasConceptScore W3211292012C119857082 @default.
• W3211292012 hasConceptScore W3211292012C12267149 @default.
• W3211292012 hasConceptScore W3211292012C124101348 @default.
• W3211292012 hasConceptScore W3211292012C154945302 @default.
• W3211292012 hasConceptScore W3211292012C169258074 @default.
• W3211292012 hasConceptScore W3211292012C41008148 @default.
• W3211292012 hasConceptScore W3211292012C52001869 @default.
• W3211292012 hasConceptScore W3211292012C80444323 @default.
• W3211292012 hasLocation W32112920121 @default.
• W3211292012 hasOpenAccess W3211292012 @default.
• W3211292012 hasPrimaryLocation W32112920121 @default.
• W3211292012 isParatext "false" @default.
• W3211292012 isRetracted "false" @default.
• W3211292012 magId "3211292012" @default.
• W3211292012 workType "dissertation" @default.