data privacy

Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer NeurIPS 2023 Generating Private Synthetic Data with Genetic Algorithms ICML 2023 HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies EMNLP 2023 Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning UAI 2023 Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data ACL 2023 Model Sparsity Can Simplify Machine Unlearning NeurIPS 2023 Multi-step Jailbreaking Privacy Attacks on ChatGPT EMNLP 2023 MUter: Machine Unlearning on Adversarially Trained Models ICCV 2023 Privacy Attacks on Schedule-Driven Data AAAI 2023 ProPILE: Probing Privacy Leakage in Large Language Models NeurIPS 2023 Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN EMNLP 2023 Static and Sequential Malicious Attacks in the Context of Selective Forgetting NeurIPS 2023 Training Data Extraction From Pre-trained Language Models: A Survey ACL 2023 Transferable Unlearnable Examples ICLR 2023 ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection NeurIPS 2022 ConfounderGAN: Protecting Image Data Privacy with Causal Confounder NeurIPS 2022 Contribution-Aware Federated Learning for Smart Healthcare AAAI 2022 Differential Privacy in Natural Language Processing: The Story So Far NAACL 2022 House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography NeurIPS 2022 Knowledge Removal in Sampling-based Bayesian Inference ICLR 2022 Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations ICLR 2022 Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling NeurIPS 2022 Private Set Generation with Discriminative Information NeurIPS 2022 Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning ICLR 2022 Should I disclose my dataset? Caveats between reproducibility and individual data rights EMNLP 2022