Input Reconstruction

Input reconstruction focuses on recovering original data from processed or transformed versions, a crucial area impacting privacy and efficiency in machine learning. Current research emphasizes developing efficient reconstruction algorithms, particularly within differentially private mechanisms and federated learning settings, often employing techniques like residual query bases, quantile-based bias initialization, and transformer-based methods. This work is vital for addressing privacy vulnerabilities in various applications, from federated learning and large language models to medical imaging, where protecting sensitive data while maintaining model performance is paramount. The development of robust reconstruction methods and effective countermeasures is crucial for ensuring responsible and secure deployment of AI systems.