  In their seminal work, Broder \textit{et. al.}~\citep{BroderCFM98} introduces
the $\mathrm{minHash}$ algorithm that computes a low-dimensional sketch of
high-dimensional binary data that closely approximates pairwise Jaccard
similarity. Since its invention, $\mathrm{minHash}$ has been commonly used by
practitioners in various big data applications. Further, the data is dynamic in
many real-life scenarios, and their feature sets evolve over time. We consider
the case when features are dynamically inserted and deleted in the dataset. We
note that a naive solution to this problem is to repeatedly recompute
$\mathrm{minHash}$ with respect to the updated dimension. However, this is an
expensive task as it requires generating fresh random permutations. To the best
of our knowledge, no systematic study of $\mathrm{minHash}$ is recorded in the
context of dynamic insertion and deletion of features. In this work, we
initiate this study and suggest algorithms that make the $\mathrm{minHash}$
sketches adaptable to the dynamic insertion and deletion of features. We show a
rigorous theoretical analysis of our algorithms and complement it with
extensive experiments on several real-world datasets. Empirically we observe a
significant speed-up in the running time while simultaneously offering
comparable performance with respect to running $\mathrm{minHash}$ from scratch.
Our proposal is efficient, accurate, and easy to implement in practice.
