Marc Juarez


School of Informatics,

University of Edinburgh

Office: IF-4.05A

10 Crichton St

Edinburgh, UK


Email: <first>.<last>

I am a Lecturer in Cyber Security and Privacy in the University of Edinburgh’s School of Informatics. Within the school, I am a member of the Security, Privacy, and Trust group (SecPrivTru). Full bio


  • Our new paper on traffic matching on mixnets will appear in PETS'24. Read our blog post!

  • Our work on the effects of retraining on AI-generated data will be featured in the Springer magazine.

  • New paper on the fair exposure problem under homophily was presented at AAAI'23 by Jakob Schoeffer.

  • I presented our new work on Privately Measuring Demographic Performance Disparities in Federated Learning. See tweet.


My research topics and a sample of relevant work:

  • ML-Based Traffic Analysis: the development and evaluation of traffic analysis defenses from both a practical and theoretical point of view.

    • S. Siby, M. Juarez, C. Diaz, N. Vallina-Rodriguez, and C. Troncoso. "Encrypted DNS ⇒Privacy? A Traffic Analysis Perspective." NDSS, 2020.

  • Security and Privacy of ML: the design of methods to audit privacy and security of ML models, and the development of privacy-aware ML techniques.

    • M. Juarez, S. Yeom, and M. Fredrikson. "Black-Box Audits for Group Distribution Shifts." arXiv pre-print, 2022.

  • Privacy and Fairness: the privacy challenges that arise from identifying and mitigating algorithmic bias.

    • M. Juarez, and A. Korolova. "'You Can’t Fix What You Can’t Measure': Privately Measuring Demographic Performance Disparities in Federated Learning." Proceedings of the Algorithmic Fairness through the Lens of Causality and Privacy Workshop (NeurIPS), 2022.

See the complete list of publications.