Artificial Intelligence Security and Privacy First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, Guangzhou, China, December 3–5, 2023, Proceedings, Part I /
This two-volume set LNCS 14509-14510, constitutes the refereed proceedings of the First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, held in Guangzhou, China, during December 3–5, 2023. The 40 regular papers and 23 workshop papers presented in this two-vo...
Bewaard in:
| Coauteur: | |
|---|---|
| Andere auteurs: | , , |
| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
|
| Editie: | 1st ed. 2024. |
| Reeks: | Lecture Notes in Computer Science,
14509 |
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/978-981-99-9785-5 |
| Tags: |
Voeg label toe
Geen labels, Wees de eerste die dit record labelt!
|
Inhoudsopgave:
- Fine-grained Searchable Encryption Scheme
- Fine-grained Authorized Secure Deduplication with Dynamic Policy
- Deep Multi-Image Hiding with Random Key
- Member Inference Attacks in Federated Contrastive Learning
- A network traffic anomaly detection method based on shapelet and KNN
- DFaP: Data Filtering and Purification Against Backdoor Attacks
- A Survey of Privacy Preserving Subgraph Matching Method
- The Analysis of Schnorr Multi-Signatures and the Application to AI
- Active Defense against Image Steganography
- Strict Differentially Private Support Vector Machines with Dimensionality Reduction
- Converging Blockchain and Deep Learning in UAV Network Defense Strategy: Ensuring Data Security During Flight
- Towards Heterogeneous Federated Learning: Analysis, Solutions, and Future Directions
- From Passive Defense to Proactive Defence: Strategies and Technologies
- Research on Surface Defect Detection System of Chip Inductors Based on Machine Vision
- Multimodal fatigue detectionin drivers via physiological and visual signals
- Protecting Bilateral Privacy in Machine Learning-as-a-Service: A Differential Privacy Based Defense
- FedCMK: An Efficient Privacy-Preserving Federated Learning Framework
- An embedded cost learning framework based on cumulative gradient
- An Assurance Case Practice of AI-enabled Systems on Maritime Inspection
- Research and Implementation of EXFAT File System Reconstruction Algorithm Based on Cluster Size Assumption and Computational Verification
- A Verifiable Dynamic Multi-Secret Sharing Obfuscation Scheme Applied to Data LakeHouse
- DZIP: A Data Deduplication-Compatible Enhanced Version of Gzip
- Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward Security
- Adversarial Attacks against Object Detection in Remote Sensing Images
- Hardware Implementation and Optimization of Critical Modules of SM9 Digital Signature Algorithm
- Post-quantum Dropout-resilient Aggregation for Federated Learning via Lattice-basedPRF
- Practical and Privacy-Preserving Decision Tree Evaluation with One Round Communication
- IoT-Inspired Education 4.0 Framework for Higher Education and Industry Needs
- Multi-agent Reinforcement Learning Based User-Centric Demand Response with Non-Intrusive Load Monitoring
- Decision Poisson: From universal gravitation to offline reinforcement learning
- SSL-ABD:An Adversarial Defense MethodAgainst Backdoor Attacks in Self-supervised Learning
- Personalized Differential Privacy in the Shuffle Model
- MKD: Mutual Knowledge Distillation for Membership Privacy Protection
- Fuzzing Drone Control System Configurations Based on Quality-Diversity Enhanced Genetic Algorithm
- KEP: Keystroke Evoked Potential for EEG-based User Authentication
- Verifiable Secure Aggregation Protocol under Federated Learning
- Electronic voting privacy protection scheme based on double signature in Consortium Blockchain
- Securing 5G Positioning via Zero Trust Architecture
- Email Reading Behavior-informed Machine Learning Model to Predict Phishing Susceptibility. .