  Machine unlearning is an emerging technology that has come to attract
widespread attention. A number of factors, including regulations and laws,
privacy, and usability concerns, have resulted in this need to allow a trained
model to forget some of its training data. Existing studies of machine
unlearning mainly focus on unlearning requests that forget a cluster of
instances or all instances from one class. While these approaches are effective
in removing instances, they do not scale to scenarios where partial targets
within an instance need to be forgotten. For example, one would like to only
unlearn a person from all instances that simultaneously contain the person and
other targets. Directly migrating instance-level unlearning to target-level
unlearning will reduce the performance of the model after the unlearning
process, or fail to erase information completely. To address these concerns, we
have proposed a more effective and efficient unlearning scheme that focuses on
removing partial targets from the model, which we name "target unlearning".
Specifically, we first construct an essential graph data structure to describe
the relationships between all important parameters that are selected based on
the model explanation method. After that, we simultaneously filter parameters
that are also important for the remaining targets and use the pruning-based
unlearning method, which is a simple but effective solution to remove
information about the target that needs to be forgotten. Experiments with
different training models on various datasets demonstrate the effectiveness of
the proposed approach.
