privacy preservation

Learning with Selective Forgetting IJCAI 2021 Matrix Sketching for Secure Collaborative Machine Learning ICML 2021 Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning CVPR 2021 Privacy-Preserving Feature Selection with Secure Multiparty Computation ICML 2021 Privacy-Preserving Voice Anti-Spoofing Using Secure Multi-Party Computation Interspeech 2021 Proactive Privacy-preserving Learning for Retrieval AAAI 2021 Regularized Mutual Learning for Personalized Federated Learning ACML 2021 Repopulating Street Scenes CVPR 2021 SAFENet: A Secure, Accurate and Fast Neural Network Inference ICLR 2021 Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating AAAI 2021 Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective CVPR 2021 Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images AAAI 2021 Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms UAI 2021 TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation NeurIPS 2021 Toward Understanding the Influence of Individual Clients in Federated Learning AAAI 2021 Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving EMNLP 2021 Adversarial Learning of Privacy-Preserving and Task-Oriented Representations AAAI 2020 A Scalable Approach for Privacy-Preserving Collaborative Machine Learning NeurIPS 2020 Assisted Learning: A Framework for Multi-Organization Learning NeurIPS 2020 AViD Dataset: Anonymized Videos from Diverse Countries NeurIPS 2020 CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks CVPR 2020 COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment AAAI 2020 Controlled Query Evaluation in Description Logics Through Instance Indistinguishability IJCAI 2020 Differentially Private Language Models Benefit from Public Pre-training EMNLP 2020 Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness EMNLP 2020