  Camera sensors are increasingly being combined with machine learning to
perform various tasks such as intelligent surveillance. Due to its
computational complexity, most of these machine learning algorithms are
offloaded to the cloud for processing. However, users are increasingly
concerned about privacy issues such as function creep and malicious usage by
third-party cloud providers. To alleviate this, we propose an edge-based
filtering stage that removes privacy-sensitive attributes before the sensor
data are transmitted to the cloud. We use state-of-the-art image manipulation
techniques that leverage disentangled representations to achieve privacy
filtering. We define opt-in and opt-out filter operations and evaluate their
effectiveness for filtering private attributes from face images. Additionally,
we examine the effect of naturally occurring correlations and residual
information on filtering. We find the results promising and believe this
elicits further research on how image manipulation can be used for privacy
preservation.
