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Related Experiment Video

Updated: Mar 27, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

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Private algorithms for the protected in social network search.

Michael Kearns1, Aaron Roth2, Zhiwei Steven Wu2

  • 1Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104 mkearns@cis.upenn.edu.

Proceedings of the National Academy of Sciences of the United States of America
|January 13, 2016
PubMed
Summary

This study presents privacy-preserving algorithms for targeted social network searches. These methods balance data privacy with societal needs like counterterrorism and disease control by minimizing privacy compromise for protected individuals.

Keywords:
counterterrorismdata privacysocial networks

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Area of Science:

  • Computational Social Science
  • Data Privacy
  • Network Analysis

Background:

  • Societal needs like counterterrorism and disease containment conflict with individual data privacy.
  • Distinguishing between protected individuals and a targeted subpopulation is often resource-intensive.

Purpose of the Study:

  • To develop a computational model and algorithms for targeted subpopulation identification in social networks.
  • To minimize privacy compromise for protected individuals while efficiently identifying targets.
  • To reduce the costs associated with traditional identification methods.

Main Methods:

  • Introduced a computational model differentiating between protected individuals and a targeted subpopulation.
  • Developed privacy-preserving algorithms based on graph search methods.
  • Incorporated noise injection into target prioritization to protect privacy.
  • Validated algorithms using extensive computational experiments on large-scale social network datasets.

Main Results:

  • Demonstrated provably privacy-preserving algorithms for targeted social network search.
  • Algorithms effectively identify targeted subpopulations with minimal privacy compromise.
  • Computational experiments validated the utility and efficiency of the proposed methods.

Conclusions:

  • The developed algorithms offer a viable solution for balancing data privacy and societal priorities.
  • Privacy-preserving targeted search in social networks is achievable.
  • The approach reduces the need for costly surveillance or testing mechanisms.