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Defeating traffic analysis via differential privacy: a case study on streaming traffic

Publication ,  Journal Article
Zhang, X; Hamm, J; Reiter, MK; Zhang, Y
Published in: International Journal of Information Security
June 1, 2022

In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the ML-powered analysis of streaming traffic. Our findings are twofold. First, constructing adversarial samples effectively confounds an adversary with a predetermined classifier but is less effective when the adversary can adapt to the defense by using alternative classifiers or training the classifier with adversarial samples. Second, differential-privacy guarantees are very effective against such statistical-inference-based traffic analysis, while remaining agnostic to the machine learning classifiers used by the adversary. We propose three mechanisms for enforcing differential privacy for encrypted streaming traffic and evaluate their security and utility. Our empirical implementation and evaluation suggest that the proposed statistical privacy approaches are promising solutions in the underlying scenarios.

Duke Scholars

Published In

International Journal of Information Security

DOI

EISSN

1615-5270

ISSN

1615-5262

Publication Date

June 1, 2022

Volume

21

Issue

3

Start / End Page

689 / 706

Related Subject Headings

  • Strategic, Defence & Security Studies
  • 46 Information and computing sciences
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences
 

Citation

APA
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ICMJE
MLA
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Zhang, X., Hamm, J., Reiter, M. K., & Zhang, Y. (2022). Defeating traffic analysis via differential privacy: a case study on streaming traffic. International Journal of Information Security, 21(3), 689–706. https://doi.org/10.1007/s10207-021-00574-3
Zhang, X., J. Hamm, M. K. Reiter, and Y. Zhang. “Defeating traffic analysis via differential privacy: a case study on streaming traffic.” International Journal of Information Security 21, no. 3 (June 1, 2022): 689–706. https://doi.org/10.1007/s10207-021-00574-3.
Zhang X, Hamm J, Reiter MK, Zhang Y. Defeating traffic analysis via differential privacy: a case study on streaming traffic. International Journal of Information Security. 2022 Jun 1;21(3):689–706.
Zhang, X., et al. “Defeating traffic analysis via differential privacy: a case study on streaming traffic.” International Journal of Information Security, vol. 21, no. 3, June 2022, pp. 689–706. Scopus, doi:10.1007/s10207-021-00574-3.
Zhang X, Hamm J, Reiter MK, Zhang Y. Defeating traffic analysis via differential privacy: a case study on streaming traffic. International Journal of Information Security. 2022 Jun 1;21(3):689–706.
Journal cover image

Published In

International Journal of Information Security

DOI

EISSN

1615-5270

ISSN

1615-5262

Publication Date

June 1, 2022

Volume

21

Issue

3

Start / End Page

689 / 706

Related Subject Headings

  • Strategic, Defence & Security Studies
  • 46 Information and computing sciences
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences