NDLI: Detecting Communities under Differential Privacy

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Detecting Communities under Differential Privacy

Content Provider	ACM Digital Library
Author	Rusinowitch, Michaël Imine, Abdessamad Nguyen, Hiep H.
Abstract	Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified that ModDivisive steadily gives the best modularity and avg.F1Score on large graphs while LouvainDP outperforms the remaining input perturbation competitors in certain settings.
Starting Page	83
Ending Page	93
Page Count	11
File Format	PDF
ISBN	9781450345699
DOI	10.1145/2994620.2994624
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Differential privacy Moddivisive Community detection Louvaindp
Content Type	Text
Resource Type	Article

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