Rishabh Poddar, UC Berkeley and Microsoft Research; Ganesh Ananthanarayanan, Srinath Setty, and Stavros Volos, Microsoft Research; Raluca Ada Popa, UC Berkeley
Video-analytics-as-a-service is becoming an important offering for cloud providers. A key concern in such services is privacy of the videos being analyzed. While trusted execution environments (TEEs) are promising options for preventing the direct leakage of private video content, they remain vulnerable to side-channel attacks.
We present Visor, a system that provides confidentiality for the user’s video stream as well as the ML models in the presence of a compromised cloud platform and untrusted co-tenants. Visor executes video pipelines in a hybrid TEE that spans both the CPU and GPU. It protects the pipeline against side-channel attacks induced by data-dependent access patterns of video modules, and also addresses leakage in the CPU-GPU communication channel. Visor is up to 1000× faster than naïve oblivious solutions, and its overheads relative to a non-oblivious baseline are limited to 2×–6×.
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author = {Rishabh Poddar and Ganesh Ananthanarayanan and Srinath Setty and Stavros Volos and Raluca Ada Popa},
title = {Visor: {Privacy-Preserving} Video Analytics as a Cloud Service},
booktitle = {29th USENIX Security Symposium (USENIX Security 20)},
year = {2020},
isbn = {978-1-939133-17-5},
pages = {1039--1056},
url = {https://www.usenix.org/conference/usenixsecurity20/presentation/poddar},
publisher = {USENIX Association},
month = aug
}
