论文标题
遮阳板：保护隐私视频分析作为云服务
Visor: Privacy-Preserving Video Analytics as a Cloud Service
论文作者
论文摘要
视频 - 分析 - 即服务正在成为云提供商的重要产品。此类服务的关键问题是被分析视频的隐私。尽管受信任的执行环境（TEE）是防止私人视频内容直接泄漏的有希望的选择，但它们仍然容易受到侧向通道攻击的影响。 我们介绍了遮阳板，该系统在存在折衷的云平台和不受信任的共同租户的情况下为用户的视频流提供机密性以及ML模型。遮阳板在跨越CPU和GPU的混合动力T恤中执行视频管道。它保护管道免受视频模块的数据依赖性访问模式引起的侧向通道攻击，并解决了CPU-GPU通信通道中的泄漏。遮阳板的$ 1000 \ times $ $ $ $ $ $ $ $ $ $，其间接费用相对于非义基线的基准限制为$ 2 \ times $ - $ 6 \ $ 6 \ times $。
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 $1000\times$ faster than naïve oblivious solutions, and its overheads relative to a non-oblivious baseline are limited to $2\times$--$6\times$.
