7 articles tagged with #privacy-preserving-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce ConfusionPrompt, a privacy framework for large language models that decomposes user prompts into smaller sub-prompts mixed with pseudo-prompts before sending to cloud servers. The method protects user privacy while maintaining higher utility than existing perturbation-based approaches and works with existing black-box LLMs without modification.
Researchers introduce EmoMAS, a Bayesian multi-agent framework that enables small language models to perform sophisticated negotiation by treating emotional intelligence as a strategic variable. The system coordinates game-theoretic, reinforcement learning, and psychological agents to optimize negotiation outcomes while maintaining privacy through edge deployment, demonstrating performance comparable to larger models across high-stakes domains.
Researchers present the first empirical study of machine unlearning in hybrid quantum-classical neural networks, adapting classical unlearning methods to quantum settings and introducing quantum-specific strategies. The study reveals that quantum models can effectively support unlearning, with performance varying based on circuit depth and entanglement structure, establishing baseline insights for privacy-preserving quantum machine learning systems.
Researchers introduce DP-MTV, the first framework enabling privacy-preserving multimodal in-context learning for vision-language models using differential privacy. The system allows processing hundreds of demonstrations while maintaining formal privacy guarantees, achieving competitive performance on benchmarks like VizWiz with only minimal accuracy loss.
Researchers successfully developed a privacy-preserving healthcare AI application that runs entirely in web browsers without downloads, using ONNX and JavaScript SDK for client-side inference. The project demonstrates how generative AI models for predicting disease risk can be deployed securely while maintaining data privacy in sensitive medical applications.
Researchers introduce FedDAG, a new clustered federated learning framework that improves AI model training across heterogeneous client environments. The system combines data and gradient similarity metrics for better client clustering and uses a dual-encoder architecture to enable knowledge sharing across clusters while maintaining specialization.
The article title suggests content about Substra, a platform for creating privacy-preserving AI systems. However, the article body appears to be empty or not provided, making detailed analysis impossible.
