4 Types Of Privacy Attacks Every Machine Learning Startup Should Watch Out For - Analytics India Magazine

With the advent of APIs that offer state-of-the-art services a click away, setting up a machine learning shop has become more accessible. But with rapid democratisation, there is a risk of non-ML players who have jumped the gun, finding themselves in a flurry of privacy attacks, never been heard of before. In a first of its kind survey carried out on ML privacy by a team from Czech Technical University, the researchers address the different ways an ML application can be vulnerable. In privacy-related attacks, wrote the researchers, an adversary's goal is related to gaining knowledge, not intended to be shared, such as knowledge about the training data or information about the model, or even extracting information about properties of the data. Black-box attacks are those attacks where the adversary does not know the model parameters, architecture or training data.