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Biblioteca

MARC Lista (tabellare)
Cyber security, privacy and networking : proceedings of ICSPN 2021 / / edited by Dharma P. Agrawal [and three others]
Cyber security, privacy and networking : proceedings of ICSPN 2021 / / edited by Dharma P. Agrawal [and three others]
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (404 pages)
Disciplina 005.8
Collana Lecture Notes in Networks and Systems
Soggetto topico Computer networks - Access control
Computer security
Data privacy
ISBN 981-16-8663-7
981-16-8664-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Organization -- Preface -- Contents -- Editors and Contributors -- A New Modified MD5-224 Bits Hash Function and an Efficient Message Authentication Code Based on Quasigroups -- 1 Introduction -- 1.1 Hash Function Without a Key -- 1.2 Hash Function with Key or HMAC -- 2 Preliminaries -- 2.1 Quasigroup -- 2.2 Optimal Quasigroups -- 2.3 Brief Description of MD5 -- 3 Proposed Schemes -- 3.1 Quasigroup Expansion (QGExp) Operation -- 3.2 Quasigroup Compression (QGComp) Operation -- 4 Implementation and Software Performance -- 5 Security Analysis -- 5.1 Analysis of QGMD5 -- 5.2 Collision Resistance -- 5.3 Avalanche Effect -- 5.4 Analysis of QGMAC -- 6 Conclusions -- References -- Leveraging Transfer Learning for Effective Recognition of Emotions from Images: A Review -- 1 Introduction -- 2 Contributions by Researchers on Human Facial Emotion Recognition -- 2.1 Feature Extraction Methods -- 2.2 Classification -- 2.3 Transfer Learning -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 Model Architectures -- 3.4 Experimental Study -- 4 Experimental Study and Comparison -- 5 Conclusion and Future Work -- References -- An Automated System for Facial Mask Detection and Face Recognition During COVID-19 Pandemic -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 Deep Learning Architecture -- 3.3 Face Recognition Module -- 4 Algorithm Used in Proposed model -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Haar Cascade Algorithm -- 5 Limitations and Future Works -- 6 RESULTS -- 6.1 Face Mask Detection Module -- 6.2 Face Recognition Module -- 7 Conclusion -- References -- ROS Simulation-Based Autonomous Navigation Systems and Object Detection -- 1 Introduction -- 2 Related Work -- 3 Robot and Environment -- 4 Software and Platforms -- 4.1 ROS -- 4.2 RDS -- 4.3 RVIZ -- 5 ROS Autonomous Navigation.
5.1 Map Creation -- 5.2 Localization -- 5.3 Path Planning -- 6 Object Detection -- 7 Results -- 7.1 Room Map Creation -- 7.2 Object Detection -- 7.3 Navigation -- 8 Conclusion and Further Work -- References -- Robotic Assistant for Medicine and Food Delivery in Healthcare -- 1 Introduction -- 2 The Robot -- 2.1 The Mechanical Implementation -- 2.2 Omnidirectional Wheels -- 2.3 Inverse Kinematic Model -- 3 Control system of the robot -- 3.1 Rotary Encoders -- 3.2 Proximity Sensors -- 3.3 Gyroscope -- 4 Testing of the Robot -- 5 Future work -- 6 Conclusions -- References -- Privacy-Preserving Record Linkage with Block-Chains -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Privacy-Preserving Record Linkage -- 3.2 Partial De-identification at Source -- 4 System Design -- 4.1 Service 1 -- 4.2 Service 2 -- 5 Performance Analysis -- 6 Security Analysis -- 7 Conclusion -- References -- Performance Analysis of Rectangular QAM Schemes Over Various Fading Channels -- 1 Introduction -- 2 Rectangular Quadrature Amplitude Modulation -- 3 Error Probability Analysis for RQAM Over Fading Channels -- 3.1 Rayleigh Fading Model -- 3.2 Rician Fading Model -- 3.3 Nakagami-m Fading Model -- 3.4 Log-Normal Fading Model -- 4 Simulation and Results -- 5 Conclusion and Future Work -- References -- New Symmetric Key Cipher Based on Quasigroup -- 1 Introduction -- 2 Preliminaries -- 2.1 Latin Squares -- 2.2 Quasigroup -- 2.3 Encryption and Decryption Using Quasigroups -- 2.4 Advanced Encryption Standard -- 3 Proposed Cipher Algorithm Structure -- 3.1 Quasigroup Selection -- 3.2 Keystream Generation -- 3.3 Encryption Algorithm -- 3.4 Decryption Algorithm -- 4 Security Analysis -- 4.1 Statistical Test for Randomness -- 5 Conclusion -- References -- Validate Merchant Server for Secure Payment Using Key Distribution -- 1 Introduction.
1.1 The Objectives of the Proposed Work Are -- 2 Related Works -- 3 System Model -- 3.1 Bilinear Mapping -- 3.2 Merchant Server Registration Process -- 3.3 Admin Server Process -- 3.4 Payment Request from Mobile User -- 3.5 Cloud Matching Process -- 4 Security Analysis of System Model -- 4.1 Man-in-Middle Attack -- 4.2 Impersonation Attack -- 5 Performance Analysis -- 6 Conclusions and Future Works -- References -- Extractive Text Summarization Using Feature-Based Unsupervised RBM Method -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 3.1 Data Pre-processing -- 3.2 Feature Extraction -- 3.3 Restricted Boltzmann Machine -- 3.4 Summary Generation -- 4 Result and Discussion -- 5 Conclusion -- References -- Depression and Suicide Prediction Using Natural Language Processing and Machine Learning -- 1 Introduction -- 2 Related Work -- 2.1 Challenges -- 3 Dataset Description and Processing -- 3.1 Dataset Preprocessing -- 4 Methodology -- 4.1 Machine Learning Classifiers -- 5 Results and Experiments -- 6 Conclusion -- References -- Automatic Detection of Diabetic Retinopathy on the Edge -- 1 Introduction -- 2 Related Work -- 3 Dataset and Pre-processing -- 4 Methods -- 4.1 ResNet 50 -- 4.2 InceptionV3 -- 4.3 EfficientNet B5 and B6 -- 4.4 VGG19 -- 5 Performance and Result -- 6 Deployment on the Edge -- 7 Conclusion and Future Scope -- References -- A Survey on IoT Security: Security Threads and Analysis of Botnet Attacks Over IoT and Avoidance -- 1 Introduction -- 1.1 IoT Security Architecture -- 2 Sources of Security Threats in IoT Applications -- 2.1 Security Issues at Sensing/Physical Layer -- 2.2 Security Issues at Data Link Layer -- 2.3 Security Issues at Network Layer -- 2.4 Security Issues at Application Layer -- 3 Common Attacks on IoT Devices -- 4 Evolution of Botnet -- 4.1 Traditional Botnets -- 4.2 IoT-Based Botnets.
4.3 Different Botnet Attacks -- 4.4 IoT Botnet Monitoring System (IBMS) -- 4.5 Bargaining and Negotiation Methodology for Botnet Identification -- 5 Conclusion and Future Enhancement -- References -- A Coherent Approach to Analyze Sentiment of Cryptocurrency -- 1 Introduction -- 2 Background -- 2.1 Cryptocurrency and Blockchain Technology -- 2.2 Twitter -- 2.3 Sentiment Analysis -- 3 Related Works -- 4 Data -- 5 Methods -- 5.1 Sentiment Analysis Using TextBlob and VADER -- 5.2 Incorporating the Output of both the VADER and TextBlob into One -- 6 Results -- 7 Conclusions and Future Plans -- References -- Supervised Machine Learning Algorithms Based on Classification for Detection of Distributed Denial of Service Attacks in SDN-Enabled Cloud Computing -- 1 Introduction -- 2 Related Work -- 3 Proposed Detection Method -- 3.1 Naive Bayes -- 3.2 Support Vector Machines -- 4 Implementation -- 5 Result and Discussion -- 6 Conclusion -- References -- Edge Computing-Based DDoS Attack Detection for Intelligent Transportation Systems -- 1 Introduction -- 2 Related Work -- 3 Proposed Mythology -- 3.1 Entropy Calculation Phase -- 3.2 Machine Learning Phase -- 4 Results and Analysis -- 4.1 Dataset Generation and Preprocessing -- 4.2 Machine Learning Analysis -- 5 Research Challenges -- 5.1 Network Slicing and Splitting -- 5.2 Side Channel Attack Protection -- 5.3 SDN-Based Detection -- 6 Conclusions and Future Work -- References -- An Empirical Study of Secure and Complex Variants of RSA Scheme -- 1 Introduction -- 2 Standard RSA Algorithm -- 3 Literature Review -- 3.1 RSA Based on Multiplicity of Public and Private Keys -- 3.2 Modified RSA Cryptosystem Based on `n' Prime Numbers -- 3.3 Enhanced RSA (ERSA) -- 4 Implementation Results and Analysis of Existing Works -- 4.1 Performance Analysis -- 5 A Multipoint Extended and Secured Parallel RSA Scheme.
5.1 Proposed Algorithm -- 6 Conclusion and Future Scope -- References -- Text Normalization Through Neural Models in Generating Text Summary for Various Speech Synthesis Applications -- 1 Introduction -- 2 Text Normalization Is a Complex Task -- 3 Previous Approaches to Text Normalization -- 3.1 Standard Approaches -- 3.2 Various Other Approaches -- 4 Proposed Model -- 5 Various Models -- 5.1 Segmentation -- 5.2 Two-Sliding Window Model -- 5.3 Provisional Sequence to Sequence Models -- 6 Universal Language Feature Covering Grammars from Various Details -- 7 Sample Results -- 8 Conclusion -- References -- Classification of Network Intrusion Detection System Using Deep Learning -- 1 Introduction -- 2 Literature Work -- 3 About Dataset -- 3.1 Data Preprocessing -- 4 Evaluation Metrics -- 5 Proposed Methodology -- 6 Conclusion -- References -- Toward Big Data Various Challenges and Trending Applications -- 1 Introduction -- 2 Big Data Processing Varieties -- 3 Big Data Challenges -- 4 Related Work -- 5 Applications Using Big Data -- 6 Conclusion -- References -- Convolutional Neural Network-Based Approach to Detect COVID-19 from Chest X-Ray Images -- 1 Introduction -- 1.1 Interdisciplinary -- 1.2 Library of Programming Function -- 1.3 Image Diagnosis -- 1.4 Edge Detection -- 2 Related Works -- 3 Existing System Architecture -- 4 Proposed System Architecture -- 4.1 Feature Engineering -- 5 Proposed Work -- 5.1 Proposed Methodology -- 6 Analysis of the Proposed Scheme -- 7 Performance Analysis of the Proposed Scheme -- 8 Conclusion -- References -- Classification of Medical Health Records Using Convolutional Neural Networks for Optimal Diagnosis -- 1 Introduction -- 2 Background -- 3 Objectives -- 4 Proposed Process Flow -- 5 Methodology -- 5.1 Dataset Collection -- 5.2 Preprocessing -- 6 Model Building -- 7 Code Snippet -- 8 Analysis of Model Performance.
9 Conclusion and Future Scope.
Record Nr. UNINA-9910743230303321
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Postan Emily <1973->  
Cambridge University Press, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Federated learning for IoT applications / / edited by Satya Prakash Yadav [and three others]
Federated learning for IoT applications / / edited by Satya Prakash Yadav [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (269 pages)
Disciplina 006.31
Collana EAI/Springer Innovations in Communication and Computing
Soggetto topico Internet of things
Machine learning
Data privacy
ISBN 3-030-85559-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910522556603321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Torra Vicenç  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
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
Engelhardt Claudia  
Göttingen : , : Universitätsverlag Göttingen, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Casas-Roma Jordi  
Barcelona : , : Editorial UOC, , [2017]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Casas-Roma Jordi  
Barcelona : , : Editorial UOC, , [2017]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Privacy in the Age of Innovation : AI Solutions for Information Security
Privacy in the Age of Innovation : AI Solutions for Information Security
Autore Palle Ranadeep Reddy
Edizione [1st ed.]
Pubbl/distr/stampa Berkeley, CA : , : Apress L. P., , 2024
Descrizione fisica 1 online resource (205 pages)
Disciplina 323.44/8
Altri autori (Persone) KathalaKrishna Chaitanya Rao
Soggetto topico Data privacy
Artificial intelligence
ISBN 979-88-6880-461-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Chapter 1: Introduction -- 1.1 The Intersection of AI, Information Security, Data Privacy, and Data Security -- 1.2 Outline of the Book -- 1.3 Target Audiences/Readers -- 1.3.1 Who Can Read This Book? -- Chapter 2: Understanding AI and Ethics -- 2.1 Fundamentals of AI, Machine Learning, and Deep Learning -- 2.1.1 Defining Artificial Intelligence -- 2.1.2 The Evolution of AI: From Rule-Based Systems to Machine Learning -- 2.1.3 Unveiling the Power of Machine Learning -- 2.1.4 Delving Deeper: Understanding Deep Learning -- 2.1.5 Privacy-Preserving Techniques in Machine Learning and Deep Learning -- 2.1.6 Ethical Considerations in AI, Machine Learning, and Deep Learning -- 2.1.7 Striking the Right Balance: Innovation and Privacy -- 2.1.8 Case Studies: AI and Privacy in Action -- 2.2 The Ethics of AI in Privacy and Security -- 2.2.1 The Intersection of Innovation and Ethics -- 2.2.2 Bias and Fairness: Addressing Ethical Quandaries -- 2.2.3 Explainability and Transparency: Fostering Trust in AI Systems -- 2.2.4 Accountability in AI: Navigating the Complex Web -- 2.2.5 Striking the Right Balance: Ethical Decision-Making in Security -- 2.2.6 Case Studies: Navigating Ethical Challenges in AI Security Applications -- 2.2.7 Navigating the Ethical Landscape of AI in Privacy and Security -- Chapter 3: Information Security and Data Privacy Landscape -- 3.1 The Current State of Information Security and Data Privacy -- 3.1.1 The Evolving Threat Landscape -- 3.1.2 Regulatory Frameworks and Compliance -- 3.1.3 Rise of Privacy-Preserving Technologies -- 3.1.4 Balancing Act: Innovation Versus Privacy -- 3.1.5 Increasing Awareness and Education -- 3.1.6 Cloud Security and the Remote Work Paradigm -- 3.1.7 Incident Response and Resilience.
Navigating the Complex Terrain -- 3.2 Key Challenges and Risks -- 3.2.1 Cybersecurity Threats: A Persistent Menace -- 3.2.2 Insider Threats: The Human Element -- 3.2.3 Regulatory Compliance Burden and Complexity of Privacy Regulations -- 3.2.4 Integration of Privacy-Preserving Technologies -- 3.2.5 Cloud Security Concerns -- 3.2.6 Advanced Persistent Threats (APTs): Stealthy Adversaries -- 3.2.7 Skills Shortage in Cybersecurity -- Navigating Complexity with Vigilance -- Chapter 4: AI for Threat Detection and Prevention -- 4.1 How AI Can Bolster Threat Detection and Mitigation -- 4.1.1 Advanced Threat Detection with Machine Learning -- 4.1.2 Predictive Analytics: Anticipating Threats Before They Manifest -- 4.1.3 Behavioral Analytics: Understanding the Human Element -- 4.1.4 Anomaly Detection: Uncovering Stealthy Threats -- 4.1.5 Natural Language Processing (NLP): Enhancing Contextual Understanding -- 4.1.6 Adversarial Machine Learning: A Cat-and-Mouse Game -- 4.1.7 Automation and Orchestration: Swift Response to Threats -- 4.1.8 Integration with Threat Intelligence Feeds -- Augmenting Security Defenses with AI -- 4.2 AI-Driven Cybersecurity Solutions -- 4.2.1 Machine Learning-Powered Endpoint Protection -- 4.2.2 Behavioral Analytics for User-Centric Security -- 4.2.3 Network Traffic Analysis with AI -- 4.2.4 Automated Threat Hunting and Incident Response -- 4.2.5 Predictive Analysis for Vulnerability Management -- 4.2.6 AI-Enhanced Phishing Detection -- 4.2.7 Autonomous Threat Intelligence Platforms -- 4.2.8 Continuous Adaptive Risk and Trust Assessment -- A Holistic Defense with AI -- 4.3 Case Studies of AI in Action -- 4.3.1 Case Study 1: Darktrace's Autonomous Response at Maersk -- 4.3.2 Case Study 2: Cylance's AI-Driven Endpoint Protection -- 4.3.3 Case Study 3: IBM Watson for Cyber Security at a Financial Institution.
4.3.4 Case Study 4: Palo Alto Networks Cortex XDR -- 4.3.5 Case Study 5: Google's AI for Phishing Detection -- 4.3.6 Case Study 6: FireEye's Helix Security Platform -- 4.3.7 Case Study 7: Symantec's AI-Enhanced Cloud Security -- Unleashing the Power of AI in Cybersecurity -- Chapter 5: Privacy-Preserving AI Techniques -- 5.1 Techniques for Preserving Privacy While Using AI -- 5.1.1 Homomorphic Encryption: Unlocking Secure Computations -- 5.1.2 Federated Learning: Decentralized Model Training -- 5.1.3 Differential Privacy: Adding Noise for Privacy Protection -- 5.1.4 Secure Multiparty Computation: Collaborative Data Analysis -- 5.1.5 Homomorphic Databases: Securing Query Processing -- 5.1.6 Zero-Knowledge Proofs: Verifying Without Revealing -- 5.1.7 Synthetic Data Generation: Mimicking Real Data Without Exposure -- Building a Privacy-Centric AI Landscape -- 5.2 Homomorphic Encryption, Differential Privacy, and Secure Multiparty Computation -- 5.2.1 Homomorphic Encryption: Preserving Confidentiality in Computation -- 5.2.2 Differential Privacy: Injecting Controlled Noise for Anonymity -- 5.2.3 Secure Multiparty Computation: Collaborative Insights Without Data Exposure -- 5.2.4 Real-World Application: Preserving Privacy in AI-Driven Healthcare Research -- 5.2.5 Overcoming Challenges: Trade-Offs and Computational Complexity -- Navigating the Privacy-AI Landscape -- 5.3 Implementing Federated Learning for Data Privacy -- 5.3.1 Understanding Federated Learning: Decentralized Intelligence -- 5.3.2 Preserving Privacy: Federated Learning in Action -- 5.3.3 Advantages of Federated Learning for Data Privacy -- 5.3.4 Real-World Applications: From Smartphones to Healthcare -- 5.3.5 Challenges and Considerations -- 5.3.6 Future Directions: Advancing Federated Learning for Privacy -- Empowering Privacy Through Federated Learning.
Chapter 6: Data Protection and Compliance -- 6.1 Regulations and Standards (e.g., GDPR, CCPA) Related to Data Privacy and Security -- 6.1.1 GDPR (General Data Protection Regulation): A Global Standard -- 6.1.2 Consumer Privacy Act of California (CCPA): Innovators in Privacy Legislation in the United States -- 6.1.3 Other International Data Protection Regulations and Standards -- 6.1.4 Navigating a Complex Environment: Compliance Challenges and Strategies -- 6.1.5 Future Legislative Trends in Data Protection -- Navigating the Regulatory Landscape -- 6.2 AI's Role in Achieving and Maintaining Compliance -- 6.2.1 Recognizing the Impact of AI on Compliance -- 6.2.2 Artificial Intelligence in Automated Data Governance -- 6.2.3 Artificial Intelligence-Driven Privacy Impact Assessments (PIAs) -- 6.2.4 Addressing Bias and Ethical Issues -- 6.2.5 Obstacles and Considerations -- 6.2.6 Future Trends: Evolution of AI-Driven Compliance -- A Synergistic Future of AI and Compliance -- Chapter 7: Securing AI Models -- 7.1 Best Practices for Securing AI Models -- 7.1.1 Strong Data Governance as the Basis -- 7.1.2 Model Development and Security Training -- 7.1.3 Operational Security and Deployment -- 7.1.4 Ethical Concerns and Bias Reduction -- 7.1.5 Education and Awareness of Users -- 7.1.6 Adherence to Regulatory Standards -- 7.1.7 Information Sharing and Collaboration -- 7.1.8 Incident Response and Recovery Planning -- 7.1.9 Emerging Technologies and Adaptive Security -- 7.1.10 Regular Security Assessments and Reviews -- A Holistic Approach to AI Model Security -- 7.2 Model Explainability and Fairness -- 7.2.1 The Importance of Model Explicability -- 7.2.2 Model Explainability Techniques -- 7.2.3 The Fairness Imperative in AI Models -- 7.2.4 Difficulties in Ensuring Model Fairness -- 7.2.5 Model Fairness Assurance Techniques.
7.2.6 Ongoing Monitoring and Bias Reduction -- 7.2.7 The Relationship Between Model Explainability and Fairness -- 7.2.8 The Way Forward: Ethical and Secure AI Models -- 7.3 Model Deployment Security -- 7.3.1 The Value of Model Deployment Security -- 7.3.2 Model Deployment Security Issues -- 7.3.3 Model Deployment Security Best Practices -- 7.3.4 Emerging Model Deployment Security Trends -- 7.3.5 Future Model Deployment Security Considerations -- Protecting AI Model Deployment's Future -- Chapter 8: Case Studies -- 8.1 Real-World Examples of AI Enhancing Information Security and Data Privacy -- 8.1.1 Healthcare Industry: Threat Prevention Using Predictive Analytics -- 8.1.2 Financial Institutions: Detection and Prevention of Fraud -- 8.1.3 E-commerce: Behavioral Analysis for Personalized Security -- 8.1.4 Manufacturing: Industrial Internet of Things Security -- 8.1.5 Government: Critical Infrastructure Protection -- 8.1.6 Social Media Platforms: Moderation of Content and User Privacy -- 8.1.7 Education Sector: Student Data Security -- 8.2 Success Stories and Lessons Learned -- 8.2.1 Financial Sector: Increasing Fraud Detection Efficiency -- 8.2.2 Technology Firm: Cloud Infrastructure Security -- 8.2.3 Healthcare Provider: AI for Patient Data Privacy Protection -- 8.2.4 Ecommerce Powerhouse: Customized Security Measures -- 8.2.5 Energy Sector: Artificial Intelligence for Predictive Maintenance and Security -- Adaptive Security Measures in Educational Institutions -- Chapter 9: AI in Data Privacy and Ethics -- 9.1 The Ethical Considerations of AI in Privacy -- 9.1.1 Balancing Innovation and Individual Anonymity Preservation -- 9.1.2 Informed Consent and Transparency -- 9.1.3 Avoiding Discrimination and Bias -- 9.1.4 Reducing Invasion -- 9.1.5 Accountability and Responsibility -- 9.1.6 Global Standards and Compliance.
9.1.7 Public Involvement and Collaboration.
Record Nr. UNINA-9910874662003321
Palle Ranadeep Reddy  
Berkeley, CA : , : Apress L. P., , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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]
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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
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