{"total":55,"rows":[{"id":"2108.04417","title":"Privacy-Preserving Machine Learning: Methods, Challenges and Directions","created":"2021-08-10","cited_by_count":58,"area":null},{"id":"2106.07229","title":"Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network","created":"2021-06-14","cited_by_count":57,"area":null},{"id":"2101.02559","title":"Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead","created":"2021-01-04","cited_by_count":41,"area":null},{"id":"2105.00529","title":"GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning","created":"2021-05-02","cited_by_count":36,"area":null},{"id":"2206.07975","title":"BlindFL: Vertical Federated Machine Learning without Peeking into Your Data","created":"2022-06-16","cited_by_count":36,"area":null},{"id":"2105.04236","title":"SIRNN: A Math Library for Secure RNN Inference","created":"2021-05-10","cited_by_count":28,"area":null},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2201.09186","title":"pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing","created":"2022-01-23","cited_by_count":20,"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":"2109.09829","title":"Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework","created":"2021-09-20","cited_by_count":17,"area":null},{"id":"2102.04661","title":"Security and Privacy for Artificial Intelligence: Opportunities and Challenges","created":"2021-02-09","cited_by_count":16,"area":null},{"id":"2107.12342","title":"Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning","created":"2021-07-26","cited_by_count":14,"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":"2212.05428","title":"ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline","created":"2022-12-11","cited_by_count":10,"area":null},{"id":"2103.16139","title":"Enabling Homomorphically Encrypted Inference for Large DNN Models","created":"2021-03-30","cited_by_count":6,"area":null},{"id":"2312.04356","title":"NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2110.11290","title":"Physical Side-Channel Attacks on Embedded Neural Networks: A Survey","created":"2021-10-21","cited_by_count":5,"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":"2112.12855","title":"SoK: Privacy-preserving Deep Learning with Homomorphic Encryption","created":"2021-12-23","cited_by_count":3,"area":null},{"id":"2112.13338","title":"MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning","created":"2021-12-26","cited_by_count":3,"area":null},{"id":"2505.11589","title":"A Training Framework for Optimal and Stable Training of Polynomial Neural Networks","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2107.05819","title":"Multitask Identity-Aware Image Steganography via Minimax Optimization","created":"2021-07-13","cited_by_count":2,"area":null},{"id":"2204.05136","title":"SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges","created":"2022-04-11","cited_by_count":2,"area":null},{"id":"2212.06322","title":"Privacy-Preserving Collaborative Learning through Feature Extraction","created":"2022-12-13","cited_by_count":2,"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":"2603.29566","title":"The Geometry of Polynomial Group Convolutional Neural Networks","created":"2026-03-31","cited_by_count":2,"area":null},{"id":"2104.02261","title":"Enabling Inference Privacy with Adaptive Noise Injection","created":"2021-04-06","cited_by_count":1,"area":null},{"id":"2109.08236","title":"Reinforcement Learning on Encrypted Data","created":"2021-09-16","cited_by_count":1,"area":null},{"id":"2112.11040","title":"Distributed Machine Learning and the Semblance of Trust","created":"2021-12-21","cited_by_count":1,"area":null},{"id":"2202.03023","title":"CECILIA: Comprehensive Secure Machine Learning Framework","created":"2022-02-07","cited_by_count":1,"area":null},{"id":"2208.02482","title":"Privacy Safe Representation Learning via Frequency Filtering Encoder","created":"2022-08-04","cited_by_count":1,"area":null},{"id":"2304.03538","title":"Adjustable Privacy using Autoencoder-based Learning Structure","created":"2023-04-07","cited_by_count":1,"area":null},{"id":"2309.04664","title":"Compact: Approximating Complex Activation Functions for Secure Computation","created":"2023-09-09","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":"2404.12512","title":"Proteus: Preserving Model Confidentiality during Graph Optimizations","created":"2024-04-18","cited_by_count":1,"area":null},{"id":"2407.18353","title":"Privacy-Preserving Hierarchical Model-Distributed Inference","created":"2024-07-25","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":"2506.15258","title":"Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2507.10494","title":"Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing","created":"2025-07-14","cited_by_count":1,"area":null},{"id":"2102.03412","title":"Machine Learning in Precision Medicine to Preserve Privacy via Encryption","created":"2021-02-05","cited_by_count":0,"area":null},{"id":"2104.00863","title":"PolyDNN: Polynomial Representation of NN for Communication-less SMPC Inference","created":"2021-04-02","cited_by_count":0,"area":null},{"id":"2105.07533","title":"Private Facial Diagnosis as an Edge Service for Parkinson's DBS Treatment Valuation","created":"2021-05-16","cited_by_count":0,"area":null},{"id":"2305.13756","title":"Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation","created":"2023-05-23","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":"2310.01664","title":"Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2403.01451","title":"Enhancing Data Provenance and Model Transparency in Federated Learning Systems -- A Database Approach","created":"2024-03-03","cited_by_count":0,"area":null},{"id":"2403.20190","title":"Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data","created":"2024-03-29","cited_by_count":0,"area":null},{"id":"2407.09955","title":"LFFR: Logistic Function For (single-output) Regression","created":"2024-07-13","cited_by_count":0,"area":null},{"id":"2411.07468","title":"Privacy-Preserving Verifiable Neural Network Inference Service","created":"2024-11-12","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":"2603.20504","title":"Meeting in the Middle: A Co-Design Paradigm for FHE and AI Inference","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.22237","title":"Decision-Aware Quadratic ReLU Replacement for HE-Friendly Inference","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2607.19146","title":"Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2608.02378","title":"Gecko: Fast Private Inference via Secure Public Encoder Offloading","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}