Near-Optimal Differentially Private k-Core Decomposition

arXiv:2312.07706v2 Announce Type: replace-cross
Abstract: Recent work by Dhulipala et al. \cite{DLRSSY22} initiated the study of the $k$-core decomposition problem under differential privacy via a connection between low round/depth distributed/parallel graph algorithms and private algorithms with small error bounds. They showed that one can output differentially private approximate $k$-core numbers, while only incurring a multiplicative error of $(2 +\eta)$ (for any constant $\eta >0$) and additive error of $\poly(\log(n))/\eps$. In this paper, we revisit this problem. Our main result is …

algorithms arxiv can cs.cr cs.ds cs.si differential privacy distributed error graph low near numbers parallel privacy private problem study under work