{"total":63,"rows":[{"id":"2109.00984","title":"CrypTen: Secure Multi-Party Computation Meets Machine Learning","created":"2021-09-02","cited_by_count":131,"area":null},{"id":"2211.01452","title":"MPCFormer: fast, performant and private Transformer inference with MPC","created":"2022-11-02","cited_by_count":69,"area":null},{"id":"2108.04417","title":"Privacy-Preserving Machine Learning: Methods, Challenges and Directions","created":"2021-08-10","cited_by_count":58,"area":null},{"id":"2104.10949","title":"CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU","created":"2021-04-22","cited_by_count":46,"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":"2112.09852","title":"LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision","created":"2021-12-18","cited_by_count":20,"area":null},{"id":"2107.00501","title":"Secure Quantized Training for Deep Learning","created":"2021-07-01","cited_by_count":15,"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":"2102.03517","title":"Privacy-Preserving Feature Selection with Secure Multiparty Computation","created":"2021-02-06","cited_by_count":12,"area":null},{"id":"2205.01423","title":"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2404.11121","title":"TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment","created":"2024-04-17","cited_by_count":11,"area":null},{"id":"2102.03513","title":"Privacy-Preserving Video Classification with Convolutional Neural Networks","created":"2021-02-06","cited_by_count":10,"area":null},{"id":"2406.03820","title":"A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2210.01639","title":"SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning","created":"2022-10-04","cited_by_count":9,"area":null},{"id":"2308.09923","title":"East: Efficient and Accurate Secure Transformer Framework for Inference","created":"2023-08-19","cited_by_count":9,"area":null},{"id":"2106.02850","title":"Tetrad: Actively Secure 4PC for Secure Training and Inference","created":"2021-06-05","cited_by_count":8,"area":null},{"id":"2206.05893","title":"Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations","created":"2022-06-13","cited_by_count":7,"area":null},{"id":"2304.11511","title":"QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model","created":"2023-04-23","cited_by_count":6,"area":null},{"id":"2408.03561","title":"MPC-Minimized Secure LLM Inference","created":"2024-08-07","cited_by_count":6,"area":null},{"id":"2102.02402","title":"SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation","created":"2021-02-04","cited_by_count":5,"area":null},{"id":"2108.07463","title":"Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation","created":"2021-08-17","cited_by_count":5,"area":null},{"id":"2201.12577","title":"Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)","created":"2022-01-29","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":"2308.09552","title":"Attesting Distributional Properties of Training Data for Machine Learning","created":"2023-08-18","cited_by_count":5,"area":null},{"id":"2404.13475","title":"PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud","created":"2024-04-20","cited_by_count":5,"area":null},{"id":"2404.16847","title":"State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey","created":"2024-02-25","cited_by_count":5,"area":null},{"id":"2105.06300","title":"Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey","created":"2021-05-13","cited_by_count":4,"area":null},{"id":"2106.02203","title":"Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation","created":"2021-06-04","cited_by_count":4,"area":null},{"id":"2201.07730","title":"SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation","created":"2022-01-19","cited_by_count":4,"area":null},{"id":"2207.02337","title":"Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2209.13643","title":"MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference","created":"2022-09-27","cited_by_count":4,"area":null},{"id":"2505.07239","title":"Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity","created":"2025-05-12","cited_by_count":4,"area":null},{"id":"2101.11835","title":"Reducing ReLU Count for Privacy-Preserving CNN Speedup","created":"2021-01-28","cited_by_count":3,"area":null},{"id":"2101.07365","title":"Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation","created":"2021-01-18","cited_by_count":2,"area":null},{"id":"2302.06361","title":"Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits","created":"2023-02-13","cited_by_count":2,"area":null},{"id":"2302.09904","title":"WW-FL: Secure and Private Large-Scale Federated Learning","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2309.04875","title":"Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference","created":"2023-09-09","cited_by_count":2,"area":null},{"id":"2101.12078","title":"S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training","created":"2021-01-28","cited_by_count":1,"area":null},{"id":"2103.03411","title":"Efficient Encrypted Inference on Ensembles of Decision Trees","created":"2021-03-05","cited_by_count":1,"area":null},{"id":"2206.12224","title":"MPClan: Protocol Suite for Privacy-Conscious Computations","created":"2022-06-24","cited_by_count":1,"area":null},{"id":"2304.10442","title":"Securing Neural Networks with Knapsack Optimization","created":"2023-04-20","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":"2312.05686","title":"Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains","created":"2023-12-09","cited_by_count":1,"area":null},{"id":"2403.11166","title":"Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption","created":"2024-03-17","cited_by_count":1,"area":null},{"id":"2406.00586","title":"VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices","created":"2024-06-02","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":"2410.09531","title":"PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization","created":"2024-10-12","cited_by_count":1,"area":null},{"id":"2506.15307","title":"SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2507.05649","title":"DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2509.25072","title":"Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications","created":"2025-09-29","cited_by_count":1,"area":null},{"id":"2511.19711","title":"CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2604.15499","title":"SecureRouter: Encrypted Routing for Efficient Secure Inference","created":"2026-04-16","cited_by_count":1,"area":null},{"id":"2108.08677","title":"Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions","created":"2021-08-19","cited_by_count":0,"area":null},{"id":"2202.02928","title":"ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning","created":"2022-02-07","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":"2403.17296","title":"Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation","created":"2024-03-26","cited_by_count":0,"area":null},{"id":"2407.00538","title":"Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging","created":"2024-06-29","cited_by_count":0,"area":null},{"id":"2412.16449","title":"CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference","created":"2024-12-21","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":"2509.09107","title":"CryptGNN: Enabling Secure Inference for Graph Neural Networks","created":"2025-09-11","cited_by_count":0,"area":null},{"id":"2512.17398","title":"DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2603.18046","title":"NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs","created":"2026-03-17","cited_by_count":0,"area":null}],"next_cursor":null}