Information Security and Privacy
Springer Nature Switzerland AG (Verlag)
978-981-96-9100-5 (ISBN)
The 54 full papers, 6 short papers and 1 invited paper included in this book were carefully reviewed and
selected from 181 submissions. They were organized in topical sections as follows: symmetric-key cryptography and cryptanalysis; public-key encryption; digital signatures and zero knowledge; cryptographic protocols and blockchain; post-quantum cryptography; homomorphic encryption and applications; cryptographic foundations and number theory; privacy enhancing technologies; AI security and privacy; system security.
.- Privacy Enhancing Technologies.
.- Comparing and Improving Frequency Estimation Perturbation Mechanisms under Local Differential Privacy.
.- Strong Federated Authentication With Password-based Credential Against Identity Server Corruption.
.- Anonymous Credentials with Credential Redaction and Its Application to SSI-based Plug Charge for Shared Vehicles.
.- Direction-Oriented Smooth Sensitivity and Its Application to Genomic Statistical Analysis.
.- Sentence Embedding Generation Method for Differential Privacy Protection.
.-KD-IBMRKE-PPFL: A Privacy-Preserving Federated Learning Framework Integrating Knowledge Distillation and Identity-Based Multi-Receiver Key
Encapsulation.
.- AI Security and Privacy.
.- Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning.
.-RAGLeak: Membership Inference Attacks on RAG-Based Large Language
Models.
.- DeGain: Detecting GAN-based Data Inversion in Collaborative Deep Learning.
.- FRFL: Fair and Robust Federated Learning Incentive Model Based on Game Theory.
.- DPFedSub: A Differentially Private Federated Learning with Randomized Subspace Descend.
.- MG-Det: Deepfake Detection with Multi-Granularity.
.- LPIA: Label Preference Inference Attack against Federated Graph Learning.
.- DARA: Enhancing Vulnerability Alignment via Adaptive Reconstruction and Dual-Level Attention.
.- Zeroth-Order Federated Private Tuning for Pretrained Large Language Models.
.- Understanding the Robustness of Machine-Unlearning Models.
.- System Security.
.- Mitigating the Unprivileged User Namespaces based Privilege Escalation Attacks with Linux Capabilities.
.- SoK: From Systematization to Best Practices in Fuzz Driver Generation.
.- Facial Authentication Security Evaluation against Deepfake Attacks in Mobile Apps.
.- Short Papers.
.- EAPIR: Efficient and Authenticated Private Information Retrieval with Fast Server Processing.
.- Ransomware Encryption Detection: Adaptive File System Analysis Against Evasive Encryption Tactics.
.- Receiver-initiated Updatable Public Key Encryption: Construction, Security and Application.
.- Robust and Privacy-Preserving Dynamic Average Consensus with Individual Weight.
.- Improving RSA Cryptanalysis: Combining Continued Fractions and Coppersmith’s Techniques.
.- Shortest Printable Shellcode Encoding Algorithm Based on Dynamic Bitwidth Selection.
.- Position Paper.
.- Bridging Clone Detection and Industrial Compliance: A Practical Pipeline for Enterprise Codebases.
| Erscheinungsdatum | 10.07.2025 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | 111 Illustrations, color; 22 Illustrations, black and white |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Kryptologie | |
| Schlagworte | Access Control • Al Security • authentication • Blockchain technology • Cloud Security • Cryptology • Cyber-physical Systems Security • Database Security • digital forensics • Key Management • Lightweight Security • loT security • Network Security • Privacy attacks and defenses • privacy enhancing technologies • Security implementation • security policy • security protocols • Software Security • System Security |
| ISBN-10 | 981-96-9100-1 / 9819691001 |
| ISBN-13 | 978-981-96-9100-5 / 9789819691005 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
