% Key papers for Encryption-based private learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_cryptonets_35781,
  title = {{CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/gilad-bachrach16.html},
}

@inproceedings{iclr2019_slalom_29143,
  title = {{Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/655},
}

@inproceedings{iclr2017_semi_28282,
  title = {{Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HkwoSDPgg},
}

@inproceedings{aaai2019_private_100205,
  title = {{Private Model Compression via Knowledge Distillation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3913},
}

@inproceedings{neurips2018_distributed_57057,
  title = {{Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11614},
}

@inproceedings{cvpr2020_synthetic_16314,
  title = {{Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2017_safetynets_56646,
  title = {{SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9244},
}

@inproceedings{cvpr2020_falcon_15514,
  title = {{FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2021_crypten_62757,
  title = {{CrypTen: Secure Multi-Party Computation Meets Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28591},
}

@inproceedings{icml2018_tapas_37028,
  title = {{TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2140},
}

@inproceedings{icml2019_low_37522,
  title = {{Low Latency Privacy Preserving Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3801},
}

@inproceedings{cvpr2021_privacy_17648,
  title = {{Privacy-Preserving Collaborative Learning With Automatic Transformation Search}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2020_cryptonas_59625,
  title = {{CryptoNAS: Private Inference on a ReLU Budget}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18981},
}

@inproceedings{neurips2016_fast_55725,
  title = {{Fast Distributed Submodular Cover: Public-Private Data Summarization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7174},
}

@inproceedings{icml2022_privacy_41028,
  title = {{Privacy for Free: How does Dataset Condensation Help Privacy?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18235},
}

@inproceedings{neurips2020_a_59266,
  title = {{A Scalable Approach for Privacy-Preserving Collaborative Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17503},
}

@inproceedings{iclr2021_capc_30535,
  title = {{CaPC Learning: Confidential and Private Collaborative Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2786},
}

@inproceedings{aaai2023_ppgencdr_4336,
  title = {{PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25566},
}

@inproceedings{neurips2019_partially_58792,
  title = {{Partially Encrypted Deep Learning using Functional Encryption}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13605},
}

@inproceedings{neurips2019_privacy_58833,
  title = {{Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13542},
}

@inproceedings{aaai2024_blind_5304,
  title = {{Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30200},
}

@inproceedings{aaai2024_merge_6572,
  title = {{MERGE: Fast Private Text Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29964},
}

@inproceedings{aaai2024_igamt_6250,
  title = {{IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29491},
}

@inproceedings{iclr2024_dp_34117,
  title = {{DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18958},
}

@inproceedings{neurips2024_open_71561,
  title = {{Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95707},
}

@inproceedings{aaai2025_a_7919,
  title = {{A Privacy-Preserving Framework for Generative Model-driven Synthetic Datasets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35222},
}

@inproceedings{icml2024_split_44819,
  title = {{Split-and-Denoise: Protect large language model inference with local differential privacy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33633},
}

@inproceedings{aaai2026_towards_108768,
  title = {{Towards Benchmarking Privacy Vulnerabilities in Selective Forgetting with Large Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41120},
}

@inproceedings{aaai2024_rrl_7061,
  title = {{RRL: Recommendation Reverse Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28782},
}

@inproceedings{aaai2024_united_7704,
  title = {{United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29620},
}

@inproceedings{aaai2025_privdnfis_10355,
  title = {{PrivDNFIS: Privacy-preserving and Efficient Deep Neuro-Fuzzy Inference System}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34222},
}

@inproceedings{iclr2025_muse_82231,
  title = {{MUSE: Machine Unlearning Six-Way Evaluation for Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29559},
}

@inproceedings{aaai2026_anti_108095,
  title = {{Anti-adversarial Learning: Desensitizing Prompts for Large Language Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40447},
}

@inproceedings{neurips2024_data_72469,
  title = {{Data Acquisition via Experimental Design for Data Markets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94889},
}

@inproceedings{aaai2024_fostering_6043,
  title = {{Fostering Trustworthiness in Machine Learning Algorithms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30286},
}

@inproceedings{aaai2024_practical_6974,
  title = {{Practical Privacy-Preserving MLaaS: When Compressive Sensing Meets Generative Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29476},
}

@inproceedings{cvpr2024_all_22751,
  title = {{All Rivers Run to the Sea: Private Learning with Asymmetric Flows}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30506},
}

@inproceedings{icml2024_caps_44719,
  title = {{CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33733},
}

@inproceedings{neurips2025_tabula_77616,
  title = {{Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Vk2sfKAdeu},
}

@inproceedings{aaai2026_reboot_107318,
  title = {{ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39670},
}