  The current trend in data regulation requirements and privacy-preserving
machine learning has emphasized the importance of machine unlearning. The naive
approach to unlearning training data by retraining over the complement of the
forget samples is susceptible to computational challenges. These challenges
have been effectively addressed through a collection of techniques falling
under the umbrella of machine unlearning. However, there still exists a lack of
sufficiency in handling persistent computational challenges in harmony with the
utility and privacy of unlearned model. We attribute this to the lack of work
on improving the computational complexity of approximate unlearning from the
perspective of the training dataset. In this paper, we aim to fill this gap by
introducing dataset condensation as an essential component of machine
unlearning in the context of image classification. To achieve this goal, we
propose new dataset condensation techniques and an innovative unlearning scheme
that strikes a balance between machine unlearning privacy, utility, and
efficiency. Furthermore, we present a novel and effective approach to
instrumenting machine unlearning and propose its application in defending
against membership inference and model inversion attacks. Additionally, we
explore a new application of our approach, which involves removing data from
`condensed model', which can be employed to quickly train any arbitrary model
without being influenced by unlearning samples. The corresponding code is
available at \href{https://github.com/algebraicdianuj/DC_U}{URL}.
