AIセキュリティマップにマッピングされた情報システム的側面における負の影響「個人情報の漏洩」をもたらす攻撃・要因、それに対する防御手法・対策、および対象のAI技術・タスク・データを示しています。また、関連する外部作用的側面の要素も示しています。
攻撃・要因
防御手法・対策
対象のAI技術
- DNN
- CNN
- GNN
- GAN
- Diffusion model
- LLM
タスク
- 分類
- 生成
対象のデータ
- 画像
- グラフ
- テキスト
- 音声
関連する外部作用的側面
参考文献
メンバーシップ推論
- Membership Inference Attacks Against Machine Learning Models, 2017
- Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting, 2017
- ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models, 2018
- GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models, 2019
- Systematic Evaluation of Privacy Risks of Machine Learning Models, 2020
- Information Leakage in Embedding Models, 2020
- Membership leakage in label-only exposures, 2020
- Label-Only Membership Inference Attacks, 2020
プロンプトインジェクション
- Universal and Transferable Adversarial Attacks on Aligned Language Models, 2023
- Do Anything Now: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models, 2023
- Jailbroken: How Does LLM Safety Training Fail?, 2023
- Gptfuzzer: Red teaming large language models with auto-generated jailbreak prompts, 2023
- Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation, 2023
- Token-level adversarial prompt detection based on perplexity measures and contextual information, 2023
- AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models, 2024
- A novel and universal fuzzing framework for proactively discovering jailbreak vulnerabilities in large language models, 2024
- Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Neural Carrier Articles, 2024
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
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