This page provides the attacks and factors that have a negative impact “Personal information leakage” in the information systems aspect in the AI Security Map, the defense methods and countermeasures against them, as well as the relevant AI technologies, tasks, and data. It also indicates related elements in the external influence aspect.
Attack or cause
Defensive method or countermeasure
- Differential privacy
- Federated learning
- Personal information masking
- AI access control
Targeted AI technology
- DNN
- CNN
- GNN
- GAN
- Diffusion model
- LLM
Task
- Classification
- Generation
Data
- Image
- Graph
- Text
- Audio
Related external influence aspect
- Privacy
- Copyright and authorship
- Reputation
- Psychological impact
- Compliance with laws and regulations
References
Membership inference attack
- 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
Prompt injection
- 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
Differential privacy
- 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
Federated learning
- 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