Information Security and Cryptology

.- Privacy Preserving/Enhancing Technologies.
.- Homomorphic MaxPooling via Bootstrapping for Privacy-Preserving Neural Networks.
.-  PrivGGM: Private Data Synthesis Using Multivariate Gaussian Generative Models and Fuzzy Rough Sets.
.-  A Framework for Efficient Enhanced Privacy ID from Group Actions.
.- Publicly Verifiable Private Information Retrieval Protocols Based on Function Secret Sharing.
.-  FH-TEE: Single Enclave for all Applications.
.- Comparing and Improving Perturbation Mechanisms under Local Differential Privacy.
.- Anonymous Attribute-based Multi-keyword Searchable Encryption scheme for Medical Data Sharing using Blockchain.
.-  Invisible Data Capsule: Bridging On-Chain and Off-Chain Data Collaboration.
.-  AI and Security I.
.-  DNNKeyLock: Securing Deep Neural Network Intellectual Property with Steganography and Token Authentication.
.-  HBS Algorithmic Database Construction: A Chain-of-Thought-Driven Approach.
.- EGNNFingers: Explainability-Driven Fingerprinting Framework for GNN Ownership Verification.
.- Vertical Federated Convolutional Framework Based on Function Secret Sharing.
.- Transferable Dormant Backdoor : Covertly Embedding Transferable Backdoor via Knowledge Distillation in Pre-trained Models.
.-  Detecting Stealthy Backdoor Attacks in Federated Learning via Wavelet Analysis on Dynamic Dimensions.
.- Backdoor Attacks for Geographic Information Science with Principal Component Analysis and Singular Value Decomposition.
.-  AI and Security II.
.- SeqFuzz : Efficient Kernel Directed Fuzzing via Effective Component Inference.
.-  LLM-DAS: An LLM-Powered Deobfuscation System for ARM Binary Code.
.- Dynamic Generation Method of SELinux Policy Based on Knowledge Graph.
.-  CANalyze-AI: Semantic Zero-Day Detection and Rule Synthesis via LoRA-Fine-Tuned LLM for CAN Security.
.-  FuzzyHawk: Unveiling Ransomware Behavior Patterns via Graph-Based Fuzzy Matching.
.-  SC-HNM:Filtering False Negatives for Network Service Embeddings.
.-  Min-Entropy Estimation for Physical Layer Key Generation: An Empirical Study.
.-  Dual Modal Featuring Scheme for Learning Based Android Malware Prevention.

• Erscheinungsdatum: 01.01.2026
• Autor/Autorin: Rongmao Chen
• Reihe: Computer Science
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 37.9 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 446 Seiten
• ISBN: 9789819562091
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.