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240301 ||| eng |
| 020 |
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|a 9789819997855
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| 100 |
1 |
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|a Vaidya, Jaideep
|e [editor]
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| 245 |
0 |
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|a Artificial Intelligence Security and Privacy
|h Elektronische Ressource
|b First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, Guangzhou, China, December 3–5, 2023, Proceedings, Part I
|c edited by Jaideep Vaidya, Moncef Gabbouj, Jin Li
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| 250 |
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|a 1st ed. 2024
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2024, 2024
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| 300 |
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|a XV, 595 p. 167 illus., 147 illus. in color
|b online resource
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| 505 |
0 |
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|a 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 --
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| 505 |
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|a 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 --
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| 505 |
0 |
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|a 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.
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| 505 |
0 |
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|a 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 --
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| 653 |
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|a Security Science and Technology
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| 653 |
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|a Security Services
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| 653 |
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|a Cryptography
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Privacy
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| 653 |
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|a Security systems
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| 653 |
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|a Data protection
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| 653 |
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|a Data encryption (Computer science)
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Data protection / Law and legislation
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| 653 |
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|a Cryptology
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| 700 |
1 |
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|a Gabbouj, Moncef
|e [editor]
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| 700 |
1 |
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|a Li, Jin
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Computer Science
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| 028 |
5 |
0 |
|a 10.1007/978-981-99-9785-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-99-9785-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a 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-volume set were carefully reviewed and selected from 115 submissions. Topics of interest include, e.g., attacks and defence on AI systems; adversarial learning; privacy-preserving data mining; differential privacy; trustworthy AI; AI fairness; AI interpretability; cryptography for AI; security applications.
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