Privacy Breach

Privacy breaches in machine learning are a growing concern, focusing on how models can inadvertently leak sensitive information about training data or users. Current research investigates vulnerabilities in various model architectures, including large language models, federated learning systems, and on-device AI, employing techniques like membership inference attacks and analysis of data distribution shifts to detect these breaches. This research is crucial for developing robust privacy-preserving techniques and ensuring responsible AI development, impacting both the ethical deployment of machine learning and the security of sensitive data across diverse applications.