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Sefik Ilkin Serengil 2025-03-10 15:09:39 +00:00 committed by GitHub
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@ -381,24 +381,20 @@ Even though vector embeddings are not reversible to original images, they still
```python ```python
from lightphe import LightPHE from lightphe import LightPHE
# define a plain vectors for source and target
alpha = DeepFace.represent("img1.jpg")[0]["embedding"]
beta = DeepFace.represent("target.jpg")[0]["embedding"]
# build an additively homomorphic cryptosystem (e.g. Paillier) on-prem # build an additively homomorphic cryptosystem (e.g. Paillier) on-prem
cs = LightPHE(algorithm_name = "Paillier", precision = 19) cs = LightPHE(algorithm_name = "Paillier", precision = 19)
# export keys
cs.export_keys("public.txt", public=True) cs.export_keys("public.txt", public=True)
# define a plain vector for source (user tower)
alpha = DeepFace.represent("img1.jpg")[0]["embedding"]
# encrypt source embedding # encrypt source embedding
encrypted_alpha = cs.encrypt(alpha) encrypted_alpha = cs.encrypt(alpha)
# restore the cryptosystem in cloud with only public key # restore the cryptosystem in cloud with only public key
cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt") cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt")
# define a plain vector for target (item tower)
beta = DeepFace.represent("target.jpg")[0]["embedding"]
# dot product of encrypted and plain embedding pair in cloud # dot product of encrypted and plain embedding pair in cloud
encrypted_cosine_similarity = encrypted_alpha @ beta encrypted_cosine_similarity = encrypted_alpha @ beta