負の影響「AIから個人情報が漏洩することでプライバシーが侵害」

差分プライバシー

• Deep Learning with Differential Privacy, 2016
• Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, 2017
• Learning Differentially Private Recurrent Language Models, 2018
• Efficient Deep Learning on Multi-Source Private Data, 2018
• Evaluating Differentially Private Machine Learning in Practice, 2019
• Tempered Sigmoid Activations for Deep Learning with Differential Privacy, 2020

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連合学習

• Practical Secure Aggregation for Federated Learning on User-Held Data, 2016
• Communication-Efficient Learning of Deep Networks from Decentralized Data, 2017
• Federated Learning: Strategies for Improving Communication Efficiency, 2018
• Federated Optimization in Heterogeneous Networks, 2020
• SCAFFOLD: Stochastic Controlled Averaging for Federated Learning, 2020
• Federated Learning with Matched Averaging, 2020

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アライメント

• Training language models to follow instructions with human feedback, 2022
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, 2022
• Constitutional AI: Harmlessness from AI Feedback, 2022
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model, 2023
• A General Theoretical Paradigm to Understand Learning from Human Preferences, 2023
• RRHF: Rank Responses to Align Language Models with Human Feedback without tears, 2023
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations, 2023
• Self-Rewarding Language Models, 2024
• KTO: Model Alignment as Prospect Theoretic Optimization, 2024
• SimPO: Simple Preference Optimization with a Reference-Free Reward, 2024

マシン・アンラーニング

• Making AI Forget You: Data Deletion in Machine Learning, 2019
• Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks, 2020
• Certified Data Removal from Machine Learning Models, 2020
• Descent-to-Delete: Gradient-Based Methods for Machine Unlearning, 2020
• Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations, 2020
• Approximate Data Deletion from Machine Learning Models, 2021
• Fast Yet Effective Machine Unlearning, 2021
• Machine Unlearning for Random Forests, 2021

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暗号化技術

• Gazelle: A Low Latency Framework for Secure Neural Network Inference, 2018
• Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference, 2018
• nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data, 2019
• Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network, 2021

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匿名化技術

• Context-Aware Generative Adversarial Privacy, 2017
• k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers, 2021

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