  With the growing use of camera devices, the industry has many image datasets
that provide more opportunities for collaboration between the machine learning
community and industry. However, the sensitive information in the datasets
discourages data owners from releasing these datasets. Despite recent research
devoted to removing sensitive information from images, they provide neither
meaningful privacy-utility trade-off nor provable privacy guarantees. In this
study, with the consideration of the perceptual similarity, we propose
perceptual indistinguishability (PI) as a formal privacy notion particularly
for images. We also propose PI-Net, a privacy-preserving mechanism that
achieves image obfuscation with PI guarantee. Our study shows that PI-Net
achieves significantly better privacy utility trade-off through public image
data.
