Cycle Counting under Local Differential Privacy for Degeneracy-bounded Graphs
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya

TL;DR
This paper introduces a local differential privacy algorithm for counting cycles in degeneracy-bounded graphs, achieving lower error rates than previous triangle counting methods, especially in social network graphs.
Contribution
The paper presents a novel algorithm for cycle counting under local differential privacy with improved error bounds for degeneracy-bounded graphs, extending to cycles of arbitrary length.
Findings
Achieves expected $ ext{ell}_2$-error of $O(n)$ in practical social networks.
Extends to approximate counts of cycles of length $k$ with similar error bounds.
Outperforms existing triangle counting algorithms in degeneracy-bounded graphs.
Abstract
We propose an algorithm for counting the number of cycles under local differential privacy for degeneracy-bounded input graphs. Numerous studies have focused on counting the number of triangles under the privacy notion, demonstrating that the expected -error of these algorithms is , where is the number of nodes in the graph. When parameterized by the number of cycles of length four (), the best existing triangle counting algorithm has an error of . In this paper, we introduce an algorithm with an expected -error of , where is the degeneracy and is the maximum degree of the graph. For degeneracy-bounded graphs () commonly found in practical social networks, our algorithm achieves an expected -error of…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Optimization and Search Problems · Distributed systems and fault tolerance
