Privacy Accuracy

Privacy-accuracy trade-offs in machine learning explore the inherent tension between protecting sensitive data and achieving high model accuracy. Current research focuses on developing differentially private mechanisms, including novel quantization techniques and optimized algorithms like DP-SGD, to mitigate this trade-off across various model architectures, such as federated learning and variational autoencoders. These efforts aim to improve the efficiency and effectiveness of privacy-preserving machine learning, impacting both the theoretical understanding of privacy guarantees and the practical deployment of AI systems in sensitive domains. A key trend is the exploration of personalized privacy approaches and the use of compression techniques to amplify privacy while maintaining accuracy.