Mallisuojat
Mallisuojat, often translated as "model protectors" or "pattern guards," is a concept primarily discussed in the context of data privacy and machine learning. It refers to techniques or mechanisms designed to prevent the unauthorized extraction or inference of sensitive information about the training data from a trained machine learning model. This is a crucial concern as models can sometimes inadvertently memorize or learn patterns from the data they were trained on, which could then be exploited by malicious actors.
The need for mallisuojat arises because deploying machine learning models, especially those trained on private or
Various approaches fall under the umbrella of mallisuojat. Differential privacy is a prominent example, where noise