Living Reference Work Entry

Encyclopedia of Algorithms

pp 1-6

Date: Latest Version

Private Analysis of Graph Data

  • Sofya  RaskhodnikovaAffiliated withComputer Science and Engineering Department, Pennsylvania State University Email author 
  • , Adam SmithAffiliated withComputer Science and Engineering Department, Pennsylvania State University


Graphs Privacy Subgraph counts Degree distribution

Years and Authors of Summarized Original Work

  • 2013; Blum, Blocki, Datta, Sheffet

  • 2013; Kasiviswanatan, Nissim, Raskhodnikova, Smith

  • 2013; Chen, Zhou

  • 2015; Raskhodnikova, Smith

  • 2015; Borgs, Chayes, Smith

Problem Definition

Many datasets can be represented by graphs, where nodes correspond to individuals and edges capture relationships between them. On one hand, such datasets contain potentially sensitive information about individuals; on the other hand, there are significant public benefits from allowing access to aggregate information about the data. Thus, analysts working with such graphs are faced with two conflicting goals: protecting privacy of individuals and publishing accurate aggregate statistics. This article describes algorithms for releasing accurate graph statistics while preserving a rigorous notion of privacy, called differential privacy.

Differential privacy was introduced by Dwork et al. [6]. It puts a restriction on the algorithm that processes sensitive data and publishes the ...

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