AI & ML New Capability

GraySense enables geospatial object tracking using only encrypted network packet sizes without any access to raw video streams.

March 31, 2026

Original Paper

Tracking without Seeing: Geospatial Inference using Encrypted Traffic from Distributed Nodes

Sadik Yagiz Yetim, Gaofeng Dong, Isaac-Neil Zanoria, Ronit Barman, Maggie Wigness, Tarek Abdelzaher, Mani Srivastava, Suhas Diggavi

arXiv · 2603.27811

The Takeaway

It demonstrates for the first time that physical motion can be reconstructed from encrypted traffic alone. This has massive implications for both privacy (side-channel attacks) and security (tracking in non-permissive network environments).

From the abstract

Accurate observation of dynamic environments traditionally relies on synthesizing raw, signal-level information from multiple distributed sensors. This work investigates an alternative approach: performing geospatial inference using only encrypted packet-level information, without access to the raw sensory data. We further explore how this indirect information can be fused with directly available sensory data to extend overall inference capabilities. We introduce GraySense, a learning-based fram