Differentially Private Community Detection over Stochastic Block Models with Graph Sketching

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There has been significant recent progress in under-standing the fundamental limits of community detection when the graph is generated from a stochastic block model (SBM). In this paper, we study the community detection problem over binary symmetric SBMs while preserving the privacy of the individual connections between the vertices.

Original languageEnglish (US)
Title of host publication2023 57th Annual Conference on Information Sciences and Systems, CISS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451819
DOIs
StatePublished - 2023
Externally publishedYes
Event57th Annual Conference on Information Sciences and Systems, CISS 2023 - Baltimore, United States
Duration: Mar 22 2023Mar 24 2023

Publication series

Name2023 57th Annual Conference on Information Sciences and Systems, CISS 2023

Conference

Conference57th Annual Conference on Information Sciences and Systems, CISS 2023
Country/TerritoryUnited States
CityBaltimore
Period3/22/233/24/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Keywords

  • Community Detection
  • Differential Privacy
  • Graph Sketching
  • Graphs
  • Subsampling

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