{"total":55,"rows":[{"id":"2304.01669","title":"Re-thinking Model Inversion Attacks Against Deep Neural Networks","created":"2023-04-04","cited_by_count":39,"area":null},{"id":"2311.08610","title":"Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption","created":"2023-11-15","cited_by_count":19,"area":null},{"id":"2410.02486","title":"Encryption-Friendly LLM Architecture","created":"2024-10-03","cited_by_count":17,"area":null},{"id":"2310.08012","title":"AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE","created":"2023-10-12","cited_by_count":15,"area":null},{"id":"2407.10886","title":"SLIP: Securing LLMs IP Using Weights Decomposition","created":"2024-07-15","cited_by_count":14,"area":null},{"id":"2403.14111","title":"HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption","created":"2024-03-21","cited_by_count":12,"area":null},{"id":"2306.00127","title":"Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning","created":"2023-05-31","cited_by_count":11,"area":null},{"id":"2111.03362","title":"A methodology for training homomorphicencryption friendly neural networks","created":"2021-11-05","cited_by_count":10,"area":null},{"id":"2302.02407","title":"HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks","created":"2023-02-05","cited_by_count":9,"area":null},{"id":"2210.15614","title":"Private and Reliable Neural Network Inference","created":"2022-10-27","cited_by_count":7,"area":null},{"id":"2510.10982","title":"Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization","created":"2025-10-13","cited_by_count":7,"area":null},{"id":"2107.12997","title":"Fully Homomorphically Encrypted Deep Learning as a Service","created":"2021-07-26","cited_by_count":6,"area":null},{"id":"2410.22307","title":"SVIP: Towards Verifiable Inference of Open-source Large Language Models","created":"2024-10-29","cited_by_count":6,"area":null},{"id":"2304.14836","title":"Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption","created":"2023-04-26","cited_by_count":5,"area":null},{"id":"2310.10349","title":"Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2312.05264","title":"All Rivers Run to the Sea: Private Learning with Asymmetric Flows","created":"2023-12-05","cited_by_count":4,"area":null},{"id":"2410.05930","title":"Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud","created":"2024-10-08","cited_by_count":4,"area":null},{"id":"2310.10665","title":"Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey","created":"2023-09-19","cited_by_count":3,"area":null},{"id":"2311.10500","title":"From Principle to Practice: Vertical Data Minimization for Machine Learning","created":"2023-11-17","cited_by_count":3,"area":null},{"id":"2512.11269","title":"A Scalable Multi-GPU Framework for Encrypted Large-Model Inference","created":"2025-12-12","cited_by_count":3,"area":null},{"id":"2309.04427","title":"Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach","created":"2023-09-08","cited_by_count":2,"area":null},{"id":"2312.12216","title":"Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data","created":"2023-12-19","cited_by_count":2,"area":null},{"id":"2401.14199","title":"MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning","created":"2024-01-25","cited_by_count":2,"area":null},{"id":"2401.15970","title":"HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference","created":"2024-01-29","cited_by_count":2,"area":null},{"id":"2408.04261","title":"Unveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding","created":"2024-08-08","cited_by_count":2,"area":null},{"id":"2110.13638","title":"EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2302.05323","title":"On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence","created":"2023-02-10","cited_by_count":1,"area":null},{"id":"2311.00959","title":"Dynamic Fair Federated Learning Based on Reinforcement Learning","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2402.11224","title":"Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvement","created":"2024-02-17","cited_by_count":1,"area":null},{"id":"2408.15231","title":"DCT-CryptoNets: Scaling Private Inference in the Frequency Domain","created":"2024-08-27","cited_by_count":1,"area":null},{"id":"2410.18114","title":"Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2411.02926","title":"Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2505.07011","title":"Source Anonymity for Private Random Walk Decentralized Learning","created":"2025-05-11","cited_by_count":1,"area":null},{"id":"2508.10836","title":"SoK: Data Minimization in Machine Learning","created":"2025-08-14","cited_by_count":1,"area":null},{"id":"2511.18976","title":"Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2208.02587","title":"Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption","created":"2022-08-04","cited_by_count":0,"area":null},{"id":"2209.06373","title":"SEEK: model extraction attack against hybrid secure inference protocols","created":"2022-09-14","cited_by_count":0,"area":null},{"id":"2210.12054","title":"Towards Global Neural Network Abstractions with Locally-Exact Reconstruction","created":"2022-10-21","cited_by_count":0,"area":null},{"id":"2304.02959","title":"When approximate design for fast homomorphic computation provides differential privacy guarantees","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2308.00963","title":"Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud","created":"2023-08-02","cited_by_count":0,"area":null},{"id":"2308.11220","title":"Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment","created":"2023-08-22","cited_by_count":0,"area":null},{"id":"2308.16530","title":"Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition","created":"2023-08-31","cited_by_count":0,"area":null},{"id":"2402.00896","title":"Privacy and Security Implications of Cloud-Based AI Services : A Survey","created":"2024-01-31","cited_by_count":0,"area":null},{"id":"2406.03405","title":"Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud","created":"2024-06-02","cited_by_count":0,"area":null},{"id":"2507.09860","title":"Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2507.14853","title":"A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption","created":"2025-07-20","cited_by_count":0,"area":null},{"id":"2507.20014","title":"Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation","created":"2025-07-26","cited_by_count":0,"area":null},{"id":"2508.12832","title":"Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds","created":"2025-08-18","cited_by_count":0,"area":null},{"id":"2509.01253","title":"Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2511.07807","title":"PRISM: Privacy-preserving Inference System with Homomorphic Encryption and Modular Activation","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.22434","title":"FastFHE: Packing-Scalable and Depthwise-Separable CNN Inference Over FHE","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2602.05410","title":"Robust Federated Learning via Byzantine Filtering over Encrypted Updates","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2602.12630","title":"TensorCommitments: A Lightweight Verifiable Inference for Language Models","created":"2026-02-13","cited_by_count":0,"area":null},{"id":"2604.16834","title":"Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2605.23096","title":"Encrypted Neural Networks without Overflows","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":null}