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Building Effective Privacy Programs : Cybersecurity from Principles to Practice
| Building Effective Privacy Programs : Cybersecurity from Principles to Practice |
| Autore | Edwards Jason |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (451 pages) |
| Disciplina | 005.8 |
| Altri autori (Persone) | WeaverGriffin |
| Soggetto topico |
Privacy-preserving techniques (Computer science)
Data protection Data privacy Computer security |
| ISBN |
1-394-34266-7
1-394-34264-0 1-394-34265-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Privacy -- Understanding Personal Data -- Data Processing -- Roles and Relationships -- Privacy Impact Assessments (PIA) -- Roles in Privacy Leadership -- Data Subject Rights (DSR) -- Privacy Frameworks and Standards -- Major Privacy Laws and Regulations -- International Privacy Concerns -- Regulatory Enforcement -- Privacy by Design and Default -- Privacy Technology and Tools -- Data Breach Management -- Emerging Privacy Trends -- Privacy Program Implementation -- Privacy Training and Awareness -- Privacy Audits & Assessments. |
| Record Nr. | UNINA-9911021977603321 |
Edwards Jason
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Embodied narratives : protecting identity interests through ethical governance of bioinformation / / Emily Postan [[electronic resource]]
| Embodied narratives : protecting identity interests through ethical governance of bioinformation / / Emily Postan [[electronic resource]] |
| Autore | Postan Emily <1973-> |
| Pubbl/distr/stampa | Cambridge University Press, 2022 |
| Descrizione fisica | 1 online resource (xiv, 296 pages) : digital, PDF file(s) |
| Disciplina | 610 |
| Collana | Cambridge bioethics and law |
| Soggetto topico |
Medical records - Access control - Psychological aspects
Personal information management - Psychological aspects Patients - Psychology Identity (Psychology) Data privacy Medical records - Law and legislation |
| Soggetto non controllato |
medico-legal research
genetic data privacy protection medical sociology |
| ISBN |
1-108-59993-1
1-108-68299-5 1-108-65259-X |
| Classificazione | LAW093000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Attending to identity -- Mapping the landscape -- Narrative self-constitution -- Bioinformation in embodied identity narratives -- Encounters with bioinformation : three examples -- Locating identity interests -- Responsibilities for disclosure -- Identity in the governance landscape. |
| Record Nr. | UNINA-9910585956003321 |
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Postan Emily <1973->
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| Cambridge University Press, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Guide to data privacy : models, technologies, solutions / / Vicenç Torra
| Guide to data privacy : models, technologies, solutions / / Vicenç Torra |
| Autore | Torra Vicenç |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 323.448 |
| Collana | Undergraduate topics in computer science |
| Soggetto topico |
Data privacy
Data protection |
| ISBN |
9783031128370
9783031128363 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996499859703316 |
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Torra Vicenç
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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How to be FAIR with your data : A teaching and training handbook for higher education institutions / / Claudia Engelhardt
| How to be FAIR with your data : A teaching and training handbook for higher education institutions / / Claudia Engelhardt |
| Autore | Engelhardt Claudia |
| Pubbl/distr/stampa | Göttingen : , : Universitätsverlag Göttingen, , 2022 |
| Descrizione fisica | 1 online resource (206 pages) : illustrations |
| Disciplina | 323.448 |
| Soggetto topico | Data privacy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | How to be FAIR with your data |
| Record Nr. | UNINA-9910567782103321 |
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Engelhardt Claudia
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| Göttingen : , : Universitätsverlag Göttingen, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Privacidad y anonimización de datos / / Jordi Casas Roma, Cristina Romero Tris ; Prólogo de David Megías Jiménez
| Privacidad y anonimización de datos / / Jordi Casas Roma, Cristina Romero Tris ; Prólogo de David Megías Jiménez |
| Autore | Casas-Roma Jordi |
| Pubbl/distr/stampa | Barcelona : , : Editorial UOC, , [2017] |
| Descrizione fisica | 1 online resource (150 pages) |
| Disciplina | 006.312 |
| Collana | Manuales (Editorial UOC) |
| Soggetto topico |
Data mining
Data privacy Data protection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | spa |
| Record Nr. | UNINA-9910795775503321 |
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Casas-Roma Jordi
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| Barcelona : , : Editorial UOC, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Privacidad y anonimización de datos / / Jordi Casas Roma, Cristina Romero Tris ; Prólogo de David Megías Jiménez
| Privacidad y anonimización de datos / / Jordi Casas Roma, Cristina Romero Tris ; Prólogo de David Megías Jiménez |
| Autore | Casas-Roma Jordi |
| Pubbl/distr/stampa | Barcelona : , : Editorial UOC, , [2017] |
| Descrizione fisica | 1 online resource (150 pages) |
| Disciplina | 006.312 |
| Collana | Manuales (Editorial UOC) |
| Soggetto topico |
Data mining
Data privacy Data protection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | spa |
| Record Nr. | UNINA-9910817828903321 |
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Casas-Roma Jordi
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| Barcelona : , : Editorial UOC, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Privacy : algorithms and society
| Privacy : algorithms and society |
| Autore | Filimowicz Michael |
| Pubbl/distr/stampa | Milton : , : Taylor & Francis Group, , 2022 |
| Descrizione fisica | 1 online resource (133 pages) |
| Disciplina | 323.448 |
| Collana | Algorithms and Society |
| Soggetto topico |
Data privacy
Personal information management Cyber intelligence (Computer security) Information technology - Social aspects |
| ISBN | 979-1-03-656980-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495992103321 |
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Filimowicz Michael
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| Milton : , : Taylor & Francis Group, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / / Youyang Qu [and three others]
| Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / / Youyang Qu [and three others] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (127 pages) |
| Disciplina | 323.448 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Data privacy
Internet of things - Security measures |
| ISBN | 981-19-1797-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996472065503316 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / / Youyang Qu [and three others]
| Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / / Youyang Qu [and three others] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (127 pages) |
| Disciplina | 323.448 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Data privacy
Internet of things - Security measures |
| ISBN | 981-19-1797-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910568275403321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Privacy, security and forensics in the Internet of Things (IoT) / / edited by Reza Montasari [and four others]
| Privacy, security and forensics in the Internet of Things (IoT) / / edited by Reza Montasari [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (219 pages) |
| Disciplina | 004.678 |
| Soggetto topico |
Digital forensic science
Internet of things Data privacy |
| ISBN | 3-030-91218-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464543703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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