Smooth Anonymity for Sparse Binary Matrices. (arXiv:2207.06358v1 [cs.CR])

When working with user data providing well-defined privacy guarantees is
paramount. In this work we aim to manipulate and share an entire sparse dataset
with a third party privately. In fact, differential privacy has emerged as the
gold standard of privacy, however, when it comes to sharing sparse datasets, as
one of our main results, we prove that \emph{any} differentially private
mechanism that maintains a reasonable similarity with the initial dataset is
doomed to have a very weak privacy guarantee. …

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