To give some context:
I am looking for a suite of techniques and tools that can theoretically enable me to conduct analysis such as classification on image datasets in a manner in which a naive observer cannot derive "much" information from an image e.g. if the image is of a person/object then the naive observer cannot tell who/what is in the image.
Naively adding noise doesn't seem to protect the content of an image, since a human observer can easily see past the noise.
I'm not aware that a machine learning task can be performed on encrypted images effectively and on naive inspection it shouldn't be possible while still maintaining cryptographic properties.
Is there any work out there on such techniques that maintain privacy of an image while still allowing it to be used for analysis?
Is what I'm after a fool's errand?