06250nam a22006375i 4500 978-981-99-9785-5 DE-He213 20251030111415.0 cr nn 008mamaa 240203s2024 si | s |||| 0|eng d 9789819997855 978-981-99-9785-5 10.1007/978-981-99-9785-5 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Artificial Intelligence Security and Privacy [electronic resource] : First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, Guangzhou, China, December 3–5, 2023, Proceedings, Part I / edited by Jaideep Vaidya, Moncef Gabbouj, Jin Li. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XV, 595 p. 167 illus., 147 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Lecture Notes in Computer Science, 1611-3349 ; 14509 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. . 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. . Artificial intelligence. 58441 Security systems. 66821 Data protection Law and legislation. 66490 Cryptography. 64299 Data encryption (Computer science). 58243 Data protection. Artificial Intelligence. 58441 Security Science and Technology. 66822 Privacy. 66493 Cryptology. 66491 Security Services. Vaidya, Jaideep. editor. edt http://id.loc.gov/vocabulary/relators/edt 70560 Gabbouj, Moncef. editor. edt http://id.loc.gov/vocabulary/relators/edt 70561 Li, Jin. editor. edt http://id.loc.gov/vocabulary/relators/edt 70562 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819997848 Printed edition: 9789819997862 Lecture Notes in Computer Science, 1611-3349 ; 14509 https://doi.org/10.1007/978-981-99-9785-5 ZDB-2-SCS ZDB-2-SXCS ZDB-2-LNC 57475 57475