  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.
