Summary
Neural networks learn patterns from labeled data to output accurate predictions on new inputs. They are increasingly embedded in critical real-world systems, from healthcare wearables and smart homes to autonomous vehicles. These systems often rely on privacy-preserving implementations that keep user inputs and model details hidden from other parties. However, both the model provider and the user face privacy and security risks. A malicious model provider can insert backdoors during training, while a malicious user can attempt intellectual property theft by reverse-engineering model parameters
