MIT's Cybersecurity Metior: A Revolutionary Approach to Data Privacy
MIT researchers have developed a new privacy metric called Probably Approximately Correct (PAC) Privacy, which allows for the addition of minimal noise to machine-learning models while still protecting sensitive data. The researchers created an algorithm that automatically determines the optimal amount of noise to add, based on the uncertainty or entropy of the original data. This approach, unlike other privacy methods, does not require knowledge of the model's inner workings or training process. The PAC Privacy algorithm guarantees privacy even against adversaries with infinite computing power. While the technique does not indicate the accuracy loss caused by the added noise, it can be improved by creating more stable machine-learning models that produce consistent outputs with subsampled data.