Billions of users manually upload their captured videos and images to cloud storage such as Dropbox and Apple iCloud straight from their camera or phone. Their private pictures and video material are subsequently stored unprotected somewhere else on some remote computer, in many cases in another country with quite different legislation. Users depend on the tools from these service providers to browse their archives of often thousands and thousands of videos and photo’s in search of some specific image or video of interest. The direct result of this is continuous exposure to cyber threats like extortion and an intrinsic loss of privacy toward the service providers. There is a perfectly valid user-centric approach possible in dealing with such confidential materials, which is to encrypt everything before uploading anything to the internet. At that point, the user may be a lot safer, but from now on would have a hard time locating any specific videos or images in their often very large collection. What if smart algorithms could describe the pictures for you, recognize who is in it and you can store this information and use it to conveniently search and share? This project develops an open source smart-gallery app which uses machine learning to recognize and tag all visual material automatically – and on the device itself. After that, the user can do what she or he wants with the additional information and the original source material. They can save them to local storage, using the tags for easy search and navigation. Or offload the content to the internet in encrypted form, and use the descriptions and tags to navigate this remote content. Either option makes images and videos searchable while fully preserving user privacy.