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Smooth Anonymity for Sparse Graphs
May 16, 2024, 4:12 a.m. | Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andres Munoz Medina
cs.CR updates on arXiv.org arxiv.org
Abstract: 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, e.g. sparse networks, as one of our main results, we prove that \emph{any} differentially private mechanism that maintains a reasonable similarity with the initial dataset is …
aim anonymity arxiv cs.cr cs.lg data dataset datasets defined differential privacy fact graphs paramount party privacy privately share sharing standard third user data work working
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