DeepU: Deeper Granular Within-Layer Machine Unlearning

Machine unlearning (MU) is a technique that removes the influence of specific data from trained AI models without retraining them from scratch, which is important for privacy laws like the right to be forgotten. DeepU is a new framework that performs fine-grained unlearning by analyzing individual weights (the parameters that make up a neural network) within each layer and categorizing them as influential, intra-dependent, or non-influential, then applying targeted updates like resetting or adjusting those weights. Tests show DeepU reduces successful membership inference attacks (where attackers try to figure out if specific data was used in training) by 60-90% while dropping accuracy by less than 3%, and it's significantly faster than other methods.