Détail du document
Identifiant
oai:arXiv.org:2402.18020
Sujet
Computer Science - Data Structures... Computer Science - Cryptography an...Auteur
Henzinger, Monika Sricharan, A. R. Zhu, LeqiCatégorie
Computer Science
Année
2024
Date de référencement
06/03/2024
Mots clés
additive error model bounds approximate mechanisms localRésumé
Computing the core decomposition of a graph is a fundamental problem that has recently been studied in the differentially private setting, motivated by practical applications in data mining.
In particular, Dhulipala et al. [FOCS 2022] gave the first mechanism for approximate core decomposition in the challenging and practically relevant setting of local differential privacy.
One of the main open problems left by their work is whether the accuracy, i.e., the approximation ratio and additive error, of their mechanism can be improved.
We show the first lower bounds on the additive error of approximate and exact core decomposition mechanisms in the centralized and local model of differential privacy, respectively.
We also give mechanisms for exact and approximate core decomposition in the local model, with almost matching additive error bounds.
Our mechanisms are based on a black-box application of continual counting.
They also yield improved mechanisms for the approximate densest subgraph problem in the local model.
Henzinger, Monika,Sricharan, A. R.,Zhu, Leqi, 2024, Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph
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