In this video, we explore the implementation of fully homomorphic encryption (FHE) for facial recognition using the DeepFace package for Python and the Tenseal library. FHE is a type of encryption that allows computations to be performed on encrypted data without the need to decrypt it first. This is particularly useful for applications such as facial recognition, where privacy and security are crucial.
We’ll walk you through the process step-by-step, demonstrating how to use the DeepFace package to create facial embeddings from images and the Tenseal library to perform FHE operations on the embeddings. We’ll also discuss the challenges and limitations of implementing FHE for facial recognition, including the computational complexity of FHE and the potential impact on performance.
By the end of the video, you’ll have a solid understanding of how to implement FHE for facial recognition using the DeepFace package and the Tenseal library. This knowledge can be applied to a variety of applications where privacy and security are important, such as healthcare, finance, and government.
Tutorial: https://sefiks.com/2021/12/01/homomorphic-facial-recognition-with-tenseal/
You may not need fully homomorphic encryption. If your requirement can be handled by partially homomorphic encryption, it would be much faster and have much smaller ciphertexts. Check out LightPHE: https://youtu.be/fh1Dgir5FXM
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