Bentley's Path

Bentley's Path is a privacy-preserving technique in machine learning that allows models to learn from data without exposing individual information. It achieves this by adding carefully calibrated noise to the training process, ensuring that the contribution of any single data point remains confidential. This method enables companies to develop accurate models while protecting user privacy, making it suitable for sensitive applications like healthcare and finance. Essentially, Bentley's Path balances data utility with privacy by integrating noise to mask specific data details, fostering secure and responsible AI development.