Recently, inference privacy has attracted increasing attention. The inference privacy concern arises most notably in the widely deployed edge-cloud video analytics systems, where the cloud needs the videos captured from the edge. The video data can contain sensitive information and subject to attack when they are transmitted to the cloud for inference. Many privacy protection schemes have been proposed. Yet, the performance of a scheme needs to be determined by experiments or inferred by analyzing the specific case. In this paper, we propose a new metric, \textit{privacy protectability}, to characterize to what degree a video stream can be protected given a certain video analytics task. Such a metric has strong operational meaning. For example, low protectability means that it may be necessary to set up an overall secure environment. We can also evaluate a privacy protection scheme, e.g., assume it obfuscates the video data, what level of protection this scheme has achieved after obfuscation. Our definition of privacy protectability is rooted in information theory and we develop efficient algorithms to estimate the metric. We use experiments on real data to validate that our metric is consistent with empirical measurements on how well a video stream can be protected for a video analytics task.

翻译：近期，推理隐私问题日益受到关注。在广泛部署的边缘-云端视频分析系统中，推理隐私问题尤为突出——云端需要边缘设备采集的视频数据进行处理。视频数据可能包含敏感信息，且在传输至云端进行推理的过程中易遭受攻击。尽管学界已提出诸多隐私保护方案，但其性能仍需通过实验验证或具体场景分析推断。本文提出一种新型度量指标——隐私可保护性（privacy protectability），用于刻画在特定视频分析任务下视频流可被保护的程度。该指标具有强烈操作意义：例如，低可保护性意味着可能需要构建全局安全环境；同时可用于评估隐私保护方案（如假设对视频数据进行混淆处理），量化混淆后方案所达到的保护层级。我们基于信息论定义隐私可保护性，并开发了高效算法以估计该指标。通过真实数据实验验证表明，该指标与视频流在视频分析任务中实际可被保护程度的经验测量结果具有一致性。