EvaluatAR

Topics: Privacy-enhancing technologies (PETs), bystander privacy, evaluation framework

Abstract

Augmented-reality headsets continuously sense their surroundings, creating privacy risks for nearby bystanders. EvaluatAR is a headset-agnostic framework that standardizes the sensor inputs, visual stimuli, and logged outputs used to evaluate visual bystander privacy-enhancing technologies. Its record- and-replay workflow lets researchers reproduce the same privacy-relevant conditions across devices and trials, compare privacy–performance trade-offs, and revisit edge cases without repeatedly recreating live human interactions. Three case studies demonstrate cross-device evaluation, support for both implicit and explicit privacy mechanisms, and rapid debugging of failures under identical inputs.

Resources

• Paper PDF
• DOI
• Presentation
• Artifacts

Citation

@article{bukhari2026evaluatar,
  title   = {EvaluatAR: A Cross-Device Evaluation Framework for Rapid Prototyping of Bystander PETs in AR},
  author  = {Bukhari, Syed Ibrahim Mustafa Shah and Corbett, Matthew and Ji, Bo and David-John, Brendan},
  journal = {Proceedings on Privacy Enhancing Technologies},
  volume  = {2026},
  number  = {4},
  pages   = {947--963},
  year    = {2026},
  doi     = {10.56553/popets-2026-0153}
}