{"total":32,"rows":[{"id":"2109.00984","title":"CrypTen: Secure Multi-Party Computation Meets Machine Learning","created":"2021-09-02","cited_by_count":131,"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":"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":"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":"2102.03513","title":"Privacy-Preserving Video Classification with Convolutional Neural Networks","created":"2021-02-06","cited_by_count":10,"area":null},{"id":"2109.10870","title":"SoK: Machine Learning Governance","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2202.07835","title":"SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service","created":"2022-02-16","cited_by_count":10,"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":"2210.07376","title":"ScionFL: Efficient and Robust Secure Quantized Aggregation","created":"2022-10-13","cited_by_count":8,"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":"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":"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":"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":"2406.02629","title":"SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud","created":"2024-06-04","cited_by_count":3,"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":"2403.03592","title":"Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training","created":"2024-03-06","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":"2102.08788","title":"ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves","created":"2021-02-17","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":"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":"2510.18109","title":"PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces","created":"2025-10-20","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":"2109.11726","title":"Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning","created":"2021-09-24","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":"2301.07474","title":"Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy","created":"2023-01-18","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":"2409.19120","title":"Secure Multiparty Generative AI","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2412.08534","title":"Protecting Confidentiality, Privacy and Integrity in Collaborative Learning","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2501.05223","title":"EVA-S2PLoR: Decentralized Secure 2-party Logistic Regression with A Subtly Hadamard Product Protocol (Full Version)","created":"2025-01-09","cited_by_count":0,"area":null},{"id":"2510.14894","title":"Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning","created":"2025-10-16","cited_by_count":0,"area":null}],"next_cursor":null}