Model Privacy

Model privacy in machine learning focuses on protecting sensitive information embedded within or accessible through trained models, aiming to prevent unauthorized data extraction or model replication. Current research emphasizes mitigating vulnerabilities in various architectures, including large language models (LLMs) and convolutional neural networks (CNNs), through techniques like differential privacy, homomorphic encryption, and zero-knowledge proofs, often addressing specific attack vectors such as membership inference and model stealing. These efforts are crucial for responsible AI deployment, balancing the benefits of powerful models with the need to safeguard user data and intellectual property.