01227nam--2200373---450-99000359500020331620111122131722.088-85020-82-8000359500USA01000359500(ALEPH)000359500USA0100035950020111122d1987----km-y0itay50------baitaIT||||||||001yyCoppa Nevigata e il suo territoriotestimonianze archeologiche dal VII al II millennio a.C.a cura di S.M. Cassano ... [et al.]RomaQuasar1987203 p.ill.24 cmIn testa al front.: Ministero per i Beni culturali e ambientali, Soprintendenza archeologica della Puglia, Università degli Studi di Roma "La Sapienza", Comunità Montana del Gargano20012001001-------2001PreistoriaFonti archeologicheBNCF930.12CASSANO,S.M.ITsalbcISBD990003595000203316AA 10,22684 DSABKDSADSA9020111122USA011317Coppa Nevigata e il suo territorio279401UNISA04994nam 2200457 450 991056827540332120231110214919.0981-19-1797-3(MiAaPQ)EBC6965069(Au-PeEL)EBL6965069(CKB)21707968500041(PPN)262168987(EXLCZ)992170796850004120221123d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPrivacy preservation in IoT machine learning approaches : a comprehensive survey and use cases /Youyang Qu [and three others]Singapore :Springer,[2022]©20221 online resource (127 pages)SpringerBriefs in Computer Science Print version: Qu, Youyang Privacy Preservation in IoT: Machine Learning Approaches Singapore : Springer,c2022 9789811917967 Intro -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 IoT Privacy Research Landscape -- 1.2 Machine Learning Driven Privacy Preservation Overview -- 1.3 Contribution of This Book -- 1.4 Book Overview -- 2 Current Methods of Privacy Protection in IoTs -- 2.1 Briefing of Privacy Preservation Study in IoTs -- 2.2 Cryptography-Based Methods in IoTs -- 2.3 Anonymity-Based and Clustering-Based Methods -- 2.4 Differential Privacy Based Methods -- 2.5 Machine Learning and AI Methods -- 2.5.1 Federated Learning -- 2.5.2 Generative Adversarial Network -- References -- 3 Decentralized Privacy Protection of IoTs Using Blockchain-Enabled Federated Learning -- 3.1 Overview -- 3.2 Related Work -- 3.3 Architecture of Blockchain-Enabled Federated Learning -- 3.3.1 Federated Learning in FL-Block -- 3.3.2 Blockchain in FL-Block -- 3.4 Decentralized Privacy Mechanism Based on FL-Block -- 3.4.1 Blocks Establishment -- 3.4.2 Blockchain Protocols Design -- 3.4.3 Discussion on Decentralized Privacy Protection Using Blockchain -- 3.5 System Analysis -- 3.5.1 Poisoning Attacks and Defence -- 3.5.2 Single-Epoch FL-Block Latency Model -- 3.5.3 Optimal Generation Rate of Blocks -- 3.6 Performance Evaluation -- 3.6.1 Simulation Environment Description -- 3.6.2 Global Models and Corresponding Updates -- 3.6.3 Evaluation on Convergence and Efficiency -- 3.6.4 Evaluation on Blockchain -- 3.6.5 Evaluation on Poisoning Attack Resistance -- 3.7 Summary and Future Work -- References -- 4 Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential Privacy -- 4.1 Overview -- 4.2 Related Work -- 4.3 Generative Adversarial Nets Driven Personalized Differential Privacy -- 4.3.1 Extended Social Networks Graph Structure -- 4.3.2 GAN with a Differential Privacy Identifier -- 4.3.3 Mapping Function.4.3.4 Opimized Trade-Off Between Personalized Privacy Protection and Optimized Data Utility -- 4.4 Attack Model and Mechanism Analysis -- 4.4.1 Collusion Attack -- 4.4.2 Attack Mechanism Analysis -- 4.5 System Analysis -- 4.6 Evaluation and Performance -- 4.6.1 Trajectory Generation Performance -- 4.6.2 Personalized Privacy Protection -- 4.6.3 Data Utility -- 4.6.4 Efficiency and Convergence -- 4.6.5 Further Discussion -- 4.7 Summary and Future Work -- References -- 5 Hybrid Privacy Protection of IoT Using Reinforcement Learning -- 5.1 Overview -- 5.2 Related Work -- 5.3 Hybrid Privacy Problem Formulation -- 5.3.1 Game-Based Markov Decision Process -- 5.3.2 Problem Formulation -- 5.4 System Modelling -- 5.4.1 Actions of the Adversary and User -- 5.4.2 System States and Transitions -- 5.4.3 Nash Equilibrium Under Game-Based MDP -- 5.5 System Analysis -- 5.5.1 Measurement of Overall Data Utility -- 5.5.2 Measurement of Privacy Loss -- 5.6 Markov Decision Process and Reinforcement Learning -- 5.6.1 Quick-Convergent Reinforcement Learning Algorithm -- 5.6.2 Best Strategy Generation with Limited Power -- 5.6.3 Best Strategy Generation with Unlimited Power -- 5.7 Performance Evaluation -- 5.7.1 Experiments Foundations -- 5.7.2 Data Utility Evaluations -- 5.7.3 Privacy Loss Evaluations -- 5.7.4 Convergence Speed -- 5.8 Summary and Future Work -- References -- 6 Future Research Directions -- 6.1 Trade-Off Optimization in IoTs -- 6.2 Privacy Preservation in Digital Twined IoTs -- 6.3 Personalized Consensus and Incentive Mechanisms for Blockchain-Enabled Federated Learning in IoTs -- 6.4 Privacy-Preserving Federated Learning in IoTs -- 6.5 Federated Generative Adversarial Network in IoTs -- 7 Summary and Outlook.SpringerBriefs in Computer Science Data privacyInternet of thingsSecurity measuresData privacy.Internet of thingsSecurity measures.323.448Qu YouyangMiAaPQMiAaPQMiAaPQBOOK9910568275403321Privacy preservation in IoT2965933UNINA02767nam0 22005413i 450 VAN0026336420240806101512.2N978364269040220230918d1984 |0itac50 baengDE|||| |||||Learning higher mathematicspt. 1. : the method of coordinates, pt. 2. : analysis of the infinitely smallLev S. Pontrjagintranslated from the Russian by Edwin HewittBerlinSpringer1984VIII, 304 p.ill.24 cm001VAN000526732001 Springer series in Soviet mathematics210 BerlinSpringerVAN00116423Metod koordinat analiz beskonechno malykh152155126-XXReal functions [MSC 2020]VANC019778MF26A03Foundations: limits and generalizations, elementary topology of the line [MSC 2020]VANC020031MF26C10Real polynomials: location of zeros [MSC 2020]VANC023638MF28-XXMeasure and integration [MSC 2020]VANC019878MF30-XXFunctions of a complex variable [MSC 2020]VANC020785MF30C15Zeros of polynomials, rational functions, and other analytic functions of one complex variable (e.g., zeros of functions with bounded Dirichlet integral [MSC 2020]VANC021625MF40-XXSequences, series, summability [MSC 2020]VANC020786MF51M05Euclidean geometries (general) and generalizations [MSC 2020]VANC023698MF51N20Euclidean analytic geometry [MSC 2020]VANC023720MFAnalysisKW:KAnalytical geometryKW:KCalculusKW:KDerivativesKW:KLearningKW:KLimit of a functionKW:KMathematicsKW:KTaylor seriesKW:KBerlinVANL000066PontrjaginLev S.VANV042247464371HewittEdwinVANV024771730Springer <editore>VANV108073650Pontrjagin, Lev SemenovicPontrjagin, Lev S.VANV060998Pontrjagin, L. S.Pontrjagin, Lev S.VANV060999ITSOL20250606RICAhttps://doi.org/10.1007/978-3-642-69040-2E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00263364BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-book 6626 08eMF6626 20230927 Metod koordinat analiz beskonechno malykh1521551UNICAMPANIA