From 46a2ca4c3c24be6cd34595c723f3d666003f28ad Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Fri, 14 Mar 2025 16:19:06 +0000 Subject: [PATCH] Update README.md typos --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 585bac4..844fda9 100644 --- a/README.md +++ b/README.md @@ -303,7 +303,7 @@ Even though vector embeddings are not reversible to original images, they still ```python from lightphe import LightPHE -# define a plain vectors for source and target +# define plain vectors for source and target alpha = DeepFace.represent("img1.jpg")[0]["embedding"] beta = DeepFace.represent("target.jpg")[0]["embedding"] @@ -323,7 +323,7 @@ calculated_similarity = cs.decrypt(encrypted_cosine_similarity)[0] print("same person" if calculated_similarity >= 1 - threshold else "different persons") ``` -In this scheme, we leverage the computational power of the cloud to compute encrypted cosine similarity. However, the cloud has no knowledge of the actual calculations it performs. That's the magic of homomorphic encryption! Only the secret key holder on the on-premises side can decrypt the encrypted cosine similarity and determine whether the pair represents the same person or different individuals. Check out [`LightPHE`](https://github.com/serengil/LightPHE) library to find out more about partially homomorphic encryption. +In this scheme, we leverage the computational power of the cloud to compute encrypted cosine similarity. However, the cloud has no knowledge of the actual calculations it performs. That's the **magic** of homomorphic encryption! Only the secret key holder on the on-premises side can decrypt the encrypted cosine similarity and determine whether the pair represents the same person or different individuals. Check out [`LightPHE`](https://github.com/serengil/LightPHE) library to find out more about partially homomorphic encryption. ## Contribution